triage-agent-qwen3b/completions/step_0175.jsonl

{"ticket_id": "TRAIN-00019", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The redirect loop for SAML SSO on jira.corp.example.com is due to clock skew between the SP (Jira) and the IdP (Okta). On the SP side, ensure the SP Entity ID in Jira matches the SP entity ID in Okta, and the ACS URL points to Jira's SAML SSO URL, which can be obtained from Okta’s metadata. On the Okta side, ensure the NameID format is 'emailAddress' (or as per Jira’s requirement). No specific configuration or certificate issues are noted from the provided context. Escalation to Okta/SAML configurations is not supported from the context given. No specific details from the given context imply further escalation is not required. Check Jira and Okta configurations for the above settings and ensure there’s no clock skew with the SAML tracer extension to see the base64 SAML response for decoding. Do not escalate.\", \"cited_artifacts\": [\"KB-00010\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00019", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the Jira.corp.example.com SSO redirect loop issue, the configuration on the Okta side seems critical as it could be a case of 'Audiences does not match' error where the SP Entity ID on the Okta side must exactly match with Jira's Audience (Audience: must exactly match what the SP expects, case-sensitive). For the Jira side, ensure the IdP SSO URL matches the Jira’s ACS URL and the NameID format is set correctly as 'emailAddress'. No direct reference found for the Okta configuration setup or corporate DNS resolution failures issues in the provided context. Escalation check is not needed. Focus on the SAML configuration and Okta side: IdP Entity ID matching Jira Audience, and Jira’s ACS URL on the Okta side for the IdP SSO URL. No additional steps provided in the context for Okta DNS resolution.\", \"cited_artifacts\": [\"KB-00010\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00019", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The redirect loop in SAML SSO for jira.corp.example.com is due to attribute mismatch between the SP and the Okta IdP. On the SP side, the IDP Entity ID (Audience) must match exactly what the SP expects ('jira.corp.example.com') and the ACS URL must match the expected 'saml/acs'. On the Okta side, ensure the NameID format is 'emailAddress' and not 'unspecified' as per the configuration checklist and error troubleshooting. For Okta and Jira setup, there's no direct solution from the provided contexts, but we can check the Clock skew by verifying the assertion attributes are correctly configured (Audience and ACS URL matching). No specific escalate is needed as all required configurations and steps are provided in the SAML configurations and troubleshooting guidance from the given contexts. No direct artifact from the provided contexts is for TLS certificates or password rotation.\", \"cited_artifacts\": [\"KB-00010\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00019", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The redirect loop issue with SAML SSO for jira.corp.example.com is due to a mismatch in the Entity ID between the SP (Jira) and the IdP (Okta). For the IdP side, ensure the SP Entity ID in Jira matches the expected Audience in Okta's metadata, which can be checked by navigating to Okta Apps and looking at the SAML IdP metadata and ensuring it matches the <audience> in Jira’s configuration. For the SP side, copy the IdP SSO URL from Okta's application settings in Okta and use it in Jira’s configuration. There are no direct fixes from the provided context for DNS resolution or TLS certificates. Escalation to other context sources for these issues is advised. No explicit reference for the redirection loop to Okta and then back to Jira due to clock skew or assertion attribute mismatch is present in the provided information. Thus, escalates without additional context on these specifics.,\", \"cited_artifacts\": [\"KB-00010\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00019", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The redirect loop in the SAML SSO for Jira.corp.example.com is due to an attribute mismatch between the SP (Jira) and the IdP (Okta) configurations. On the Jira side, the SP entity ID must match the IdP’s audience and be case-sensitive. Ensure that the Jira Entity ID in the Okta integration is correct and that the IdP nameID format matches Jira’s settings. On the Okta side, Jira should be listed in the Okta SAML App configuration under the Audience (Audience URL). No direct escalation is needed from the given context but there’s no concrete guidance on Okta Okta configuration. No such specific context for Okta Okta configurations or Okta Okta Okta configurations. Escalation of help in context is needed from Okta side.\", \"cited_artifacts\": [\"KB-00010\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00019", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The SSO redirect loop is due to a clock skew between the IdP (Okta) and the SP (Jira). On the SP side, ensure the IdP Entity ID matches the SP Audience, and the ACS URL is correctly set in Jira's SAML configuration. Use SAML Tracer to decode the SAMLResponse and check for 'Audiences does not match' or 'InResponseTo mismatch' for common errors. On the Okta side, ensure the internal Okta tenant settings match Jira’s ACS URL for the SP SSO URL. For Okta's naming discrepancy (NameID format), set it to 'unspecified' as per Jira’s configuration requirement. No specific actions from the provided contexts can handle DNS resolution, certificate renewal issues, or service account rotation, hence these tickets can be escalated without direct resolutions. Escalation ticket needed for other potential factors. No specific context directly addresses Okta direct settings or proxy configurations which Okta might use to send data which is causing the loop.\", \"cited_artifacts\": [\"KB-00010\"], \"confidence\": 0.1, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00019", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the Jira.corp.example.com redirect loop issue in SAML SSO, the user-side configuration is clear — the IdP Entity ID and the SP Audience must match and the NameID format should be 'emailAddress'. On the Jira side, the audience must be exactly the IdP ACS URL and the SP must set the IdP SSO URL correctly in the metadata, aligning the Audience. The network-side checks reveal no direct applicability and the SSL certificates are not relevant here. No specific escalation steps from the provided context apply; hence, we focus on correct configuration between the IdP and SP side. Escalation not required as none of the provided steps apply directly to the network or service account rotation issues. Escalation is false with no steps required from the provided context.\", \"cited_artifacts\": [\"KB-00010\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00019", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The SSO redirect loop in Jira.corp.example.com could be due to an attribute mismatch between the SP (Jira) and IdP (Okta). On the Jira side, the SP Entity ID and the ACS URL must match the Okta SAML configuration on the Okta side. Use the SAML Tracer Chrome extension to check the POST to the ACS URL and decode the SAMLResponse Base64 payload to ensure that the 'Audiences' and 'InResponseTo' fields match between the SP and IdP configurations. For Okta, the SP SSO URL and the IdP Signing Certificate need to be the exact match with Okta's IdP metadata. No specific escalation issues from the provided context. Escalation not required.\", \"cited_artifacts\": [\"KB-00010\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00008", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the AD account lockout issue with jsmith, follow the steps in KB-00008: Step 1 — Check lockout status: Use `Get-ADUser <username> -Properties LockedOut,BadLogonCount,LastBadPasswordAttempt` to see if the account is locked (LockedOut=True) and unlock it with `Unlock-ADAccount -Identity <username>`. For Step 2 — Find lockout source, use `Event ID 4740` to see the source of the lockout on the PDC Emulator (Event ID 4740 on PDC Emulator). For Step 3 — Password expiry, use `Get-ADUser <username> -Properties PasswordExpired` to check if there are any Password Expiry issues (PasswordExpired=True) and reset with `Set-ADAccountPassword`. No escalation needed as the steps are detailed in KB-00008.\", \"cited_artifacts\": [\"KB-00008\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00008", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For jsmith's AD account lockout issue, follow the Active Directory Authentication Failures Troubleshooting guide (KB-00008) for resolution. Use the steps to check lockout status: `Get-ADUser jsmith -Properties LockedOut,BadLogonCount,LastBadPasswordAttempt` to check the lockout status and unlock: `Unlock-ADAccount -Identity jsmith` if LockedOut is True. Ensure the password is not based on a locked account (BadLogonCount and LastBadPasswordAttempt). For password expiry, use `Get-ADUser jsmith -Properties PasswordExpired`. If PasswordExpired is True, set the password again: `Set-ADAccountPassword -Identity jsmith -NewPassword <newpassword>` and ensure there’s no clock skew by running `w32tm /resynctime`, else adjust the clock using `w32tm /resynctime`. No escalation needed from the provided contexts.\", \"cited_artifacts\": [\"KB-00008\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00008", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For user jsmith’s AD account lockout every 10-15 minutes, change the password and enable locking status checks to unlock the account immediately with `Unlock-ADAccount -Identity <username>` in PowerShell. For preventing lockouts, ensure the password isn’t locked too soon and avoid repeating the same password every 90 days to align with the 'password expiry' and 'BadLogonCount' parameters with `Set-ADAccountPassword -Identity <username> -Reset` without repeating the password within 90 days. No direct fix from the Okta or Google Workspace MFA reset procedures are applicable. For checking lockout source and source computers locking the account, use `Get-ADUser <username> -Properties LockedOut,BadLogonCount,LastBadPasswordAttempt` and `Event ID 4740 on the PDC Emulator` with the tool LockoutStatus.exe, both are from KB-00008 and the Active Directory authentication failures step guide. No reference to BGP peer sessions down in the context, hence no direct fix for this from the retrieved context.\", \"cited_artifacts\": [\"KB-00008\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00008", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For jsmith's AD account lockout, use the following steps from the Active Directory authentication failures troubleshooting guide (KB-00008) for address the issue: Use `Get-ADUser <username> -Properties LockedOut,BadLogonCount,LastBadPasswordAttempt` to check the lockout status, and unlock the account using `Unlock-ADAccount -Identity <username>`. To ensure the password isn’t being locked due to a cached old password on a mapped drive, use the Account Lockout Status (LockoutStatus.exe) tool provided in the Active Directory Authentication Failures — Troubleshooting guide (KB-00008). No need to escalate as the context provides a step-by-step guide on account lockout status and unlocking an account without external manual rules changing. No need to refer to the other provided resources for this specific ticket.\", \"cited_artifacts\": [\"KB-00008\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00008", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For jsmith’s AD account getting locked out every 10-15 minutes due to an account lockout issue, follow these steps: For Step 1 — Check lockout status: Use `Get-ADUser <username> -Properties LockedOut,BadLogonCount,LastBadPasswordAttempt` to check if the account is locked. If LockedOut=True, unlock with `Unlock-ADAccount -Identity <username>`. For Step 2 — Find lockout source: Use `Get-ADUser <username> -Properties AccountLockoutStatus` for source; Event ID 4740 on the PDC Emulator will show the source computer locking the account. For Step 3 — Password expiry: Use `Get-ADUser <username> -Properties PasswordExpired` to check if the account is locked due to password expiration; reset it with `Unlock-ADAccount -Identity <username>` if True. No need for escalation as the retrieved context directly addresses the issue in Steps 1, 2, and 3 and does not refer to escalatory steps or external procedures.\", \"cited_artifacts\": [\"KB-00008\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00008", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the AD account lockout issue, use Step 1 in the Active Directory Authentication Failures Troubleshooting from KB-00008: Check the lockout status with `Get-ADUser <username> -Properties LockedOut,BadLogonCount,LastBadPasswordAttempt` in PowerShell and unlock with `Unlock-ADAccount -Identity <username>`. For the password expiry, reset it with `Set-ADAccountPassword`. No external steps from other tickets are needed, as all these are directly provided under the Active Directory Authentication Failures section.\", \"cited_artifacts\": [\"KB-00008\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 175}
{"ticket_id": "TRAIN-00008", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the AD account lockout issue where jsmith keeps getting locked out and unlocked every 10-15 minutes, follow the Active Directory Authentication Failures Troubleshooting guide in KB-00008: Use the PowerShell cmdlet `Get-ADUser <username> -Properties LockedOut, BadLogonCount, LastBadPasswordAttempt` to check the lockout status. Unlock the account with `Unlock-ADAccount -Identity <username>`. For a lockout source, use `Event ID 4740` from the PDC Emulator and use the Account Lockout Status tool for further diagnostics. No need for escalation as the context directly addresses the lockout and unlock patterns and does not mention any direct MFA or network issues from the provided contexts. \", \"cited_artifacts\": [\"KB-00008\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00008", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For user jsmith in AD, the account keeps getting locked every 10-15 minutes due to password expiry and cached old password being used. Use the Step 3 from KB-00008 Active Directory Authentication Failures — Troubleshooting to reset the password via `Set-ADAccountPassword` or unlock the account with `Unlock-ADAccount -Identity <username>`. For password expiry, reset it with `Set-ADAccountPassword`. No escalation needed as the steps are clearly provided. No additional references found in the context.\", \"cited_artifacts\": [\"KB-00008\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 175}
{"ticket_id": "TRAIN-00010", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For Michael Torres, the Okta Admin Console should be used to reset his MFA by navigating to the People section and clicking on the user (Michael Torres). Then, under More Actions, select 'Reset Multifactor' and confirm the reset with the Okta Admin Console. For Google Workspace, use the command `gam update user <email> is2svEnrolled false` and notify the user to re-enroll at myaccount.google.com/signinoptions/two-step-verification. No escalation to other procedures is required as the steps are clearly provided under the MFA reset and account unlock procedures from KB-00011. Escalation to another context is not applicable here.\", \"cited_artifacts\": [\"KB-00011\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00010", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For user Michael Torres locked out of MFA due to Okta Admin Console, follow these steps: Go to Okta Admin Console > Directory > People > Search for user Michael Torres > Click on user > More Actions > Reset Multifactor. Confirm the reset and ensure a video call or badge scan verification is done before resetting. Use Okta Admin Console steps as documented: Okta MFA reset. For Okta Admin Console > Directory > People > Search user > Click user > More Actions > Reset Multifactor and confirm reset. No escalation needed from the provided contexts. For Google Workspace MFA, use the command: `gam update user <email> is2svEnrolled false` and notify the user to re-enroll at myaccount.google.com/signinoptions/two-step-verification. No specific Okta or Azure AD / Entra reset for Michael’s case since no direct steps are listed there. Escalation not applicable as the solution is detailed in Okta Admin Console reset and Okta MFA reset procedures from KB-00011 and Okta Admin Console steps from Okta Admin Console for MFA reset. \", \"cited_artifacts\": [\"KB-00011\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00010", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For Michael Torres, who is locked out of the MFA due to Okta Admin Console, go to the Okta Admin Console and navigate to the People section, search for the user Michael Torres, and click on the More Actions. Then select ‘Reset Multifactor’. Confirm the reset by ensuring an activation email is sent to Michael and guiding him to re-enroll at myaccount.google.com/signinoptions/two-step-verification as per the Okta MFA reset procedure. There’s no need to escalate as the Okta MFA reset and Azure AD MFA reset procedures are explicitly detailed for IT admins to reset MFA and users in the Okta Admin Console and Azure portal. No specific Active Directory or SAML configurations are mentioned in the context, so these are not relevant for the resolution. Escalation is not needed as all required procedures are outlined in the Okta Admin Console and Okta MFA reset procedure with Okta Admin Console steps followed.\", \"cited_artifacts\": [\"KB-00011\"], \"confidence\": 0.5, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00010", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For Michael Torres locked out of MFA with the Okta Admin Console, follow these steps: Go to the Okta Admin Console > Directory > People and search for the user Michael Torres. Click on the user > More Actions and select Reset Multifactor. Confirm the reset by clicking on 'Yes' in the Okta Admin Console and then notify the user to re-enroll on myaccount.google.com/signinoptions/two-step-verification. If using Azure AD, use the Azure Portal > Users > Select user > Authentication methods > Require re-register MFA or via PowerShell: Set-MgUserAuthenticationRequirement -UserId <objectId> -PerUserMfaState Disabled, and ensure to always check the user's Okta session by using the Okta Admin Console > Directory > People > Search user and clicking on 'More Actions' to check 'Current Sessions' and terminate all before resetting the MFA. No escalation required from the given context.\", \"cited_artifacts\": [\"KB-00011\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00010", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For Michael Torres, the Okta Admin Console should be used to reset the MFA by going to the People section and searching for the user <email>. Click on the user and choose 'More Actions' > 'Reset Multifactor'. Confirm the reset and the user will receive an activation email and must re-enroll at myaccount.google.com/signinoptions/two-step-verification. No escalation is needed as the steps are clearly outlined under the Okta MFA reset in KB-00011.\", \"cited_artifacts\": [\"KB-00011\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 175}
{"ticket_id": "TRAIN-00010", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For Michael Torres who lost his phone with the Google Authenticator app and is locked out of MFA due to the Okta Admin Console procedure, follow these steps: Go to the Okta Admin Console and navigate to Directory > People > Search for Michael Torres. Click on the user > More Actions > Reset Multifactor. Confirm the reset by clicking 'Submit Reset.' This is an Okta MFA reset procedure for the Okta Admin Console, which can be found in KB-00011, and ensures the user is locked out and the IT admin resets MFA without the user being locked out. Do not reset if there are active sessions that could be hijacked, and notify the user to re-enroll using myaccount.google.com/signinoptions/two-step-verification. No other actions are required from the Azure Portal or the CyberArk vault procedures. Escalation is not required as all solutions are available in the provided context.\", \"cited_artifacts\": [\"KB-00011\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00010", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For Michael Torres to reset his MFA, an IT admin should follow the Okta MFA reset procedure from KB-00011: go to the Okta Admin Console > Directory > People and search for Michael Torres. Click on the user > More Actions > Reset Multifactor, confirm the reset, and the user will receive an activation email to re-enroll in the Okta application. No changes are required from the Azure AD or Google Workspace MFA resets since Okta-specific procedures are provided. No need to escalate to backup verification codes or Active Directory troubleshooting due to the specific Okta procedures given. Escalation to service account password rotation is not applicable to the MFA reset request as it pertains to enterprise user authentication, not Okta MFA.\", \"cited_artifacts\": [\"KB-00011\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00010", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For Michael Torres, since his Okta app is the MFA provider and he lost his phone, an Okta Admin needs to reset the MFA for Michael via the Okta Admin Console. Go to Directory > People > Search for Michael. Click on Michael > More Actions > Reset Multifactor. Confirm the reset and the user will receive an activation email. User must then re-enroll at myaccount.google.com/signinoptions/two-step-verification. No other tool or steps are directly applicable from the given information as the Okta MFA reset procedure is the most pertinent method from KB-00011.\", \"cited_artifacts\": [\"KB-00011\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00040", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the Lenovo T16 laptops not recognizing the built-in fingerprint reader as a FIDO2 authenticator, there is no direct reference to FIDO2 or fingerprint reader issues in the provided contexts. The provided contexts relate to Okta SCIM provisioning, SAML 2.0 SSO configuration, and Service Account Password Rotation, which are unrelated to the fingerprint reader issue in the Lenovo T16 laptops. Thus, there is no specific ticket resolution from the given contexts.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 175}
{"ticket_id": "TRAIN-00040", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The issue with the Lenovo T16 fingerprint reader not being recognized as a FIDO2 authenticator is unrelated to the provided Okta SCIM 2.0 provisioning setup, SAML 2.0 configuration, or Okta ADFS IdP configuration. There is no information in the given context to resolve the FIDO2 authenticator compatibility issue with the fingerprint reader on Lenovo T16 hardware. No artifact from the provided context can be used for the resolution. Escalation advised due to the lack of relevant information.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 175}
{"ticket_id": "TRAIN-00040", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The issue with the Lenovo T16 laptops not recognizing the built-in fingerprint reader as a FIDO2 authenticator for corporate SSO is not directly covered by any of the provided contexts. There is no clear common attribute configuration or service account password rotation mentioned, and no explicit reference to fingerprint reader issues or the built-in fingerprint reader in the given contexts. Escalation to additional information or specific context related to fingerprint readers is required.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 175}
{"ticket_id": "TRAIN-00040", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the FIDO2/WebAuthn passkey issue on Lenovo T16 hardware, there is no direct resolution provided in the retrieved contexts. However, the fingerprint reader issue isn't related to Okta SCIM provisioning or SAML 2.0 SSO, so we need to look for a hardware-related fix. Since the T16 hardware fingerprint reader isn't being recognized as a FIDO2 authenticator, we need a hardware driver or BIOS setting for the fingerprint reader that supports FIDO2. No direct context points to such a hardware or driver-related issue. Escalation of this without additional software or hardware context is not possible with the available information.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00040", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The issue with the FIDO2/WebAuthn passkey not working on Lenovo T16 laptops due to the fingerprint reader not being recognized as a FIDO2 authenticator appears to be unrelated to the Okta and SAML configurations discussed. There is no direct relevant information within the given context to address the hardware fingerprint reader issue with Lenovo T16 laptops using FIDO2. No specific ticket resolutions are mentioned that directly tackle this hardware FIDO2 authenticator recognition problem based on the provided information. Escalation to another set of context might be required to fully resolve this issue.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00040", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The issue with the fingerprint reader on Lenovo T16 laptops not being recognized as a FIDO2 authenticator is not directly related to the provided contexts. There is no information available in the given contexts that would address the issue with the fingerprint reader on the Lenovo T16 hardware and the built-in fingerprint reader being treated as a FIDO2 authenticator. Escalation to other resources or expert advice is required.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 175}
{"ticket_id": "TRAIN-00040", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The issue with the FIDO2 passkey not being recognized on new Lenovo T16 laptops due to fingerprint reader not being recognized as a FIDO2 authenticator does not appear to be directly related to the context provided. There's no direct resolution within the given information for the fingerprint reader issue or any connection to Okta SCIM provisioning setup or SAML 2.0 SSO configuration. Escalation to other resources or more detailed context on FIDO2 authenticators and Lenovo T16 fingerprint reader compatibility is required for a resolution.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 175}
{"ticket_id": "TRAIN-00040", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The issue with the FIDO2/WebAuthn passkey not being recognized on new Lenovo T16 hardware with the built-in fingerprint reader is related to hardware recognition of the authenticator and not related to any of the Okta, SAML, or CyberArk services mentioned. No artifact from the provided context can be used to resolve the hardware recognition issue for FIDO2 authenticators on the Lenovo T16 model. Escalation is required due to the lack of direct fixable reference in the provided context.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 175}
{"ticket_id": "TRAIN-00023", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The BGP peer session dropping every 2 hours due to a hold-timer mismatch is resolved by ensuring the hold-timer on both routers is consistent (via `neighbor <ip> timers <keepalive> <hold>`). For the firewall ACL changes causing this, there's no direct hit in the provided context. Escalation to the firewall team is required to ensure the ACL does not inadvertently affect the BGP hold-timer settings. Refer to the BGP peer session down troubleshooting guide from KB-00001 for handling the hold-timer settings and the firewall ACL changes to the firewall team for resolving the peer settings. No direct fix is offered for the firewall team changes due to the lack of provided context for these.”\", \"cited_artifacts\": [\"KB-00001\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00023", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the BGP peer session down to AS65002 dropping every 2 hours, the main reason is an AS number mismatch in the BGP hold timer with the peer. Use `neighbor <ip> timers <keepalive> <hold>` to adjust hold timers on the router to ensure the hold time matches the peer AS configuration. For configuration mismatch, use `neighbor <ip> remote-as <peer-as-number>` to ensure both AS numbers are the same. No specific guidance from the contexts given for firewall ACL changes or the Bangkok issue's specifics, but the consistent advice from KB-00001 could apply. Escalation not required as we can resolve with BGP settings without adding any artifacts from the retrieved context.\", \"cited_artifacts\": [\"KB-00001\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00023", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the BGP peer session dropping every 2 hours, the issue is due to a mismatch in the hold timer between your local router and peer AS. Use `clear ip bgp neighbors timers hold` to reset the hold timer configuration and ensure `neighbor peer-ip timers hold` aligns with the hold time on the peer AS. Escalation to troubleshooting other issues like AS number mismatch and configuration are not applicable from the given context. Watch out for hold timer mismatch, which can be fixed with `neighbor peer-ip timers hold` and ensure AS numbers match with `neighbor peer-ip remote-as` or check configuration through `show ip ospf neighbors detail` for mismatch.\", \"cited_artifacts\": [\"KB-00001\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00023", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the BGP peer session down issue, the configuration mismatch leading to hold-timer mismatch should be resolved by fixing the hold-timer mismatch with `neighbor <ip> timers <keepalive> <hold>` or clear ACL blocking TCP/179. Ensure that the peer's local AS number (remote-as) matches the peer's local AS configuration (neighbor <ip> remote-as <AS>). Escalation to troubleshooting is not needed as the context directly guides to the fix for hold-timer mismatch. No specific artifact cites for BGP peer session down as direct fix steps are available in the 'KB-00001' article. No escalation options needed.\", \"cited_artifacts\": [\"KB-00001\"], \"confidence\": 0.1, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00023", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the BGP peer session dropping every 2 hours issue, fix the hold-timer mismatch by using `neighbor <peer-ip> timers <keepalive> <hold>` on the router to synchronize the hold-timer between the local router and peer AS to ensure a hold time of 180 seconds (matching Cisco IOS hold time of 90 seconds). No specific mention of firewall ACL changes affecting BGP peer sessions, so we focus on BGP configuration mismatch. Escalation not required as direct resolution steps are provided under KB-00001 for BGP peer session down troubleshooting guide.\", \"cited_artifacts\": [\"KB-00001\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 175}
{"ticket_id": "TRAIN-00023", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the BGP peer session dropping every 2 hours for AS65002 due to hold-timer mismatch, correct the hold-timer issue with `neighbor <ip> timers <keepalive> <hold>` on the router or clear the ACL blocking TCP/179 if the peer address is from the VRF: `ping vrf MGMT <peer-ip> source <local-ip>` and check for `hold-timer = keepalive`. No need for escalate as the direct resolution is present in the context under KB-00001 for BGP peer session down troubleshooting guide.\", \"cited_artifacts\": [\"KB-00001\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 175}
{"ticket_id": "TRAIN-00023", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the BGP peer session flapping issue where the peer address (peer-ip) drops every 2 hours with the configuration mismatch due to AS number or MD5 password mismatch, correct the AS number configuration match with `neighbor <ip> timers <keepalive> <hold>` or clear ACLs on both ends blocking TCP/179 (as per KB-00001). Ensure there is no MD5 mismatch by validating the peer configuration and checking for AS number consistency and MD5 configuration (similar to BGP peer session issue with the given context). No need for escalation as the context provides clear steps for resolving the hold-timer mismatch and configuration AS number consistency.\", \"cited_artifacts\": [\"KB-00001\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00023", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the BGP peer session flapping every 2 hours, the correct troubleshooting method is to ensure there is no configuration mismatch by matching the local AS with the peer's local AS and ensuring there is no MD5 password mismatch by checking the configuration and the AS numbers. Use the command `neighbor <ip> remote-as <AS>` to align the local AS of the peer with the local AS of the AS number peer is configured for in the BGP configuration and use `show ip ospf neighbors detail` to check for BGP peer state and ‘hold time’ mismatch. Escalation of the provided data is not necessary. No explicit reference from the provided context for Phase 1 or AD authentication errors.\", \"cited_artifacts\": [\"KB-00001\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00042", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The Aruba ClearPass NAC blocking the Bosch wearable sensor is not directly covered by the provided runbooks or contexts. However, we can look for device fingerprint recognition policies and ensure the ClearPass policy does not block new IoT device categories by adjusting ClearPass policies or checking if there’s an exception for device categories that are not recognized. No specific runbooks or context directly guide the Aruba ClearPass policy configuration or device fingerprinting recognition which isn’t present in the provided texts. Escalation is not required as the issues are more related to ClearPass policies and less to device categories like the runbooks suggest.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00042", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the Aruba ClearPass NAC blocking new Bosch wearable sensors issue, there is no direct runbook or runbook available from the provided context. However, we can infer that the Aruba ClearPass NAC policy might need updating to add the new Bosch sensor category as per the requirement. No exact runbooks or runbooks are given from the context that can be directly applied. Escalation for specific policies or category updates in Aruba ClearPass can be manual policy setup or checking the ClearPass Admin Portal settings for a new device category. No specific error runbooks or runbooks are provided in the context.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00042", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"There isn't a direct runbook or clear reference to the issue of Aruba ClearPass NAC blocking a new device category in the provided context. Aruba ClearPass NAC blocking a new IoT device category isn't mentioned in the retrieved information. No runbooks, clear steps or references for this scenario are available based on the provided context.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 175}
{"ticket_id": "TRAIN-00042", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The Aruba ClearPass NAC blocking of the Bosch wearable sensor due to it not being added to the ClearPass NAC category is an out-of-scope issue based on the provided retrieved context. There’s no direct runbook or steps mentioned for ClearPass NAC device categories in the given information. Escalation to expert knowledge or a different runbook is required for this device class inclusion issue.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 175}
{"ticket_id": "TRAIN-00042", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The Aruba ClearPass NAC blocking the Bosch wearable sensors could be due to the DHCP scope exhaustion preventing the devices from getting IP addresses due to address pool exhaustion. Run the following steps to address this: For immediate mitigation, reduce the DHCP lease time to one day (if the current is 8 days): `ip dhcp pool CORP_WIFI / lease 1`. For a permanent fix, expand the DHCP scope by converting the current /24 to /23 (if it's full): `ip dhcp pool CORP_WIFI / range 10.0.0.1 10.0.0.254` or adding a new secondary pool: `ip dhcp pool CORP_WIFI_2 / range 10.0.1.1 10.0.1.254`. No runbook or escalate from the provided context directly addresses the ClearPass NAC policy issue for the new device category, as there is no direct resolution provided in the retrieved context for ClearPass NAC or Active Directory issues that relate to the given context points.\", \"cited_artifacts\": [\"KB-00006\"], \"confidence\": 0.0, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00042", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Since the Aruba ClearPass NAC issue with Bosch wearable sensors is related to the device category not being recognized and there being no policy defined, we need to look at policies and device categories in Aruba ClearPass. We have no direct runbooks or solutions from the provided contexts that directly address this issue. However, we can infer that the system's policies need to be updated to include the new category of Bosch wearable sensors. There’s no direct policy configuration mentioned in the provided contexts; therefore, we must look for solutions beyond the provided runbooks. For the Aruba ClearPass NAC policy update, we can seek an Aruba support document or policy document from the Aruba ClearPass documentation portal. No specific IDs can be cited from the given contexts.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00042", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The Aruba ClearPass NAC blocking the Bosch wearable sensors is an issue of device category classification in the ClearPass policy, which does not appear to be addressable by the provided runbooks on VPN tunnel flapping, Active Directory authentication failures, or DHCP scope exhaustion. No direct runbooks are given in the provided context for device classification in ClearPass NAC policies. Escalation to expert advice or an Aruba ClearPass policy runbook on device classification would be required to resolve this issue.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 175}
{"ticket_id": "TRAIN-00042", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"No clear runbook or direct resolution is present in the provided retrieved context for the Aruba ClearPass NAC blocking the Bosch wearable sensor. The provided runbooks do not address the issue of device categories and policy set for new IoT devices by Aruba ClearPass NAC. There are no direct steps or runbooks mentioned from the provided context that can be used to resolve this issue.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 175}
{"ticket_id": "TRAIN-00041", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"There is no direct runbook or context in the provided context that addresses the Kafka consumer group lag spike due to a Rust application, or details on database connection pools exhaustion relevant to this scenario. The runbook for Rust application or DB connection pool exhaustion runbooks are missing from the provided contexts. Therefore, we cannot definitively resolve this ticket from the given information, and escalation to detailed monitoring or further runbooks specific to Rust applications or DB connection pools is required. No immediate cited artifacts are available from the provided contexts that can be used for resolution.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00041", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The message lag issue for the Kafka consumer group 'recommendation-consumer' is likely due to an unmanaged thread pool exhaustion in the Rust application that consumes the Kafka topic 'recommendations'. As there are no runbooks available for this specific issue, we should look at the API Gateway 504 Timeout Troubleshooting runbook. Check the API Gateway timeout configuration with the following steps: Identify the slow upstream service with the access logs (Step 1 of KB-00018): `grep 504 /var/log/kong/access.log | awk '{print $7, $9}' | sort -n` and filter for 504s, bypass the gateway and hit the upstream service with `curl -w '%{time_total}' -o /dev/null http://<upstream-host>:<port>/endpoint` to see if the upstream service is slowing down the response. We need to escalate to get the specific runbook run for the application pool exhaustion which is not provided directly here, hence resolving this with the API Gateway runbook first. Escalation to specific connection pool exhaustion runbook isn't clear from provided runbooks.\", \"cited_artifacts\": [\"KB-00018\"], \"confidence\": 0.1, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00041", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The issue with the Kafka consumer group lag spike is not directly mentioned in the provided contexts, but there is a possibility related to connection pool exhaustion in the application logs (KB-00016) as the problem does not match the runbook scenarios given for BGP peer session down or API Gateway timeout issues. There is no clear runbook for Kafka consumer group issues like the one for the Rust application in the provided context. Escalation is required as the context does not provide a direct runbook or run-to-conclusion guidance on the Rust application’s consumer group lag spike in Kafka.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00041", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The issue with the Kafka consumer group recommendation-consumer lagging with a 4.2M message queue on the recommendations topic isn't covered by the provided runbooks or steps. Since there's no runbook for the Rust application Rust consumer in Kafka and the provided runbooks address issues like database connection pool exhaustion and API gateway timeout, no clear solution can be inferred from the given context. Escalation of this ticket cannot be handled with the provided information.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 175}
{"ticket_id": "TRAIN-00041", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"There is no direct runbook for the Kafka consumer group lag spike problem from the provided context. However, the Rust application could be holding too many connections in a pool, leading to the message lag. Check the connection pool exhaustion using Prometheus to monitor `hikaricp_connections_active` and `hikaricp_connections_max` and `hikaricp_connections_pending`. For the Rust app, there's no specific runbook from the provided context, but addressing connection pool exhaustion could mitigate the consumer group lag spike. No other runbooks or runtable are present in the provided context for the Kafka consumer lag spike.\", \"cited_artifacts\": [\"KB-00016\"], \"confidence\": 0.1, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00041", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the Kafka consumer group lag spike on the recommendations topic, no runbook exists and the context provided does not correlate with the Rust application’s message lag. There’s no direct runbook or indicators in the context that correlate with the Rust application, Rust consumer group message lag, or the specific topic of ‘recommendations’. No relevant information from the retrieved context can resolve this issue. Escalation to deeper knowledge or a runbook specifically for such scenarios is needed.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 175}
{"ticket_id": "TRAIN-00041", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The consumer group lag spike with the Rust application on the Kafka consumer group recommendation-consumer is a database connection pool exhaustion issue. Use Step 1 — Confirm pool exhaustion from KB-00016: Check Prometheus/Grafana metric 'hikaricp_connections_active' and 'hikaricp_connections_max' to confirm pool saturation and 'RUNNABLE' or 'WAITING' state threads in thread dumps from 'kill -3 <pid>.' No runbook for Kafka consumer group is provided in the retrieved context, so no runbook use can be recommended. Escalation is not required as runbooks are found for the Rust application database connection pool exhaustion and no match for the Kafka consumer group runbook. Escalation steps and attributes are not applicable.\", \"cited_artifacts\": [\"KB-00016\"], \"confidence\": 0.1, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00041", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The Kafka consumer group message lag is not directly related to the provided runbooks since there are no direct runbooks or related issues in the context for Kafka consumer group lag or Rust application lag. However, if the Rust application is waiting on a database connection, we can look into DB Connection Pool Exhaustion (KB-00016). Check if the `hikaricp_connections_active` metric in Prometheus/Grafana equals `hikaricp_connections_max` and that the `hikaricp_connections_pending` count is not zero. On the database side, check the `pg_stat_activity` command for PostgreSQL or `SHOW PROCESSLIST` for MySQL on the DB side to see if there are idle connections causing a pool exhaustion. There are no runbooks for the Rust application in this context, so no direct escalation. No specific runbook directly applies to Kafka consumer group lag, and it is advised to check the Rust application for thread pool exhaustion and use the API Gateway Timeout Troubleshooting guide (KB-0018) to identify if the Rust application is waiting on a timeout dependent on upstream services. No specific runbook from retrieved context directly corresponds to the Kafka consumer lag, and there is no escalation path provided.\", \"cited_artifacts\": [\"KB-00016\"], \"confidence\": 0.0, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00026", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the Redis eviction and key expiry issues, adjust the eviction policy to 'allkeys-lru' using the configuration: `redis-cli CONFIG SET maxmemory-policy allkeys-lru` to ensure Redis evicts least-recently-used keys first. For API gateway 504 timeout, the upstream response time should be less than the configured timeout in the API gateway. Check the `upstream_response_time` in the API gateway access logs and filter for 504s: `grep 504 /var/log/kong/access.log | awk '{print $7, $9}' | sort -n`. If timeouts are due to DB query regression, check the explain plan on slow queries and ensure there’s no missing index. For thread pool exhaustion, ensure Prometheus/Grafana metric `hikaricp_connections_active` does not equal `hikaricp_connections_max` and check the connections pending in the pool. No clear escalation from the given context, but the issue is specific to Redis eviction and API gateway timeout as per the provided solutions.\", \"cited_artifacts\": [\"KB-00019\", \"KB-00018\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00026", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the Redis eviction issue, the eviction policy is set to 'noeviction' (default) which causes Redis to return an OOM error due to keys not being evicted under memory pressure as per KB-00019. For the API gateway 504 timeout, the upstream service does not respond within the gateway's configured timeout and the service is waiting on its dependency (DB query regression or thread pool exhaustion). For Redis eviction, set the eviction policy to 'allkeys-lru' in Spring Session by adding 'spring.session.redis.cleanup-cron=0 * * * * *' to clear the keys periodically. For the 504 timeout, identify the slow upstream by checking `upstream_response_time` in /var/log/kong/access.log and test the upstream directly using `curl -w '%{time_total}' -o /dev/null http://<upstream-host>:<port>/endpoint`. Check for DB query regression with `explain plan on slow queries` and check connection pool exhaustion with `hikaricp_connections_active` and `hikaricp_connections_max` in Prometheus/Grafana metrics or application logs. No escalation required from the given contexts.\", \"cited_artifacts\": [\"KB-00019\", \"KB-00018\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00026", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the Redis memory pressure causing evictions issue, check the eviction policy with `redis-cli CONFIG GET maxmemory-policy` and ensure `maxmemory` is not set too low. If `maxmemory` is high, Redis is under memory pressure causing eviction. For the 504 timeout in the API gateway, filter the API gateway access logs for 'upstream_response_time' to find slow upstream services and use `curl` to test the upstream directly. For the API gateway timeout, check `upstream_response_time` filter in logs: `grep 504 /var/log/kong/access.log | awk '{print $7, $9}' | sort -n`, and adjust timeout config to `proxy_read_timeout 60000` in the configuration file. No direct clue on the connection pool exhaustion from the provided info. Escalation by context is not applicable as all relevant issues are covered by the retrieved contexts. No direct reference for Kubernetes CrashLoopBackOff from the given data.\", \"cited_artifacts\": [\"KB-00019\", \"KB-00018\"], \"confidence\": 0.4, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00026", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the Redis cache eviction and key expiry issues causing cache misses to spike, switch to allkeys-lru eviction policy by running `redis-cli CONFIG SET maxmemory-policy allkeys-lru` for the Spring Session configuration in the application context. For API Gateway timeout (504) issues, filter API gateway access logs for 504s: `grep 504 /var/log/kong/access.log | awk '{print $7, $9}' | sort -n` to identify if the upstream service is timing out or waiting on its dependency causing thread pool exhaustion. Adjust the KONG proxy timeout configuration in the ingress service configuration to `proxy_read_timeout 60000` in the YAML file to avoid Gateway timeout. Escalation tools are not needed as both issues can be resolved with the provided KB-00019 for eviction and KB-00018 for timeout fixes.\", \"cited_artifacts\": [\"KB-00019\", \"KB-00018\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00026", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For Redis memory pressure causing evictions, use KB-00019 for the eviction misconfiguration and KB-00019 for the Redis eviction policies. For the API gateway timeout, use KB-00018 to identify slow upstreams by analyzing the gateway access logs and upstream response times, and bypassing the gateway to check directly for the slow upstream service by using `curl` commands. No specific escalation is needed from the provided context. No IDs are available from the context directly for 504 timeout as they relate to API gateway timeout and not directly to Redis evictions or API gateway configuration issues in the provided contexts.\", \"cited_artifacts\": [\"KB-00019\", \"KB-00018\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00026", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the Redis eviction causing evictions and API gateway 504 timeouts, for the Redis issue, use KB-00019 for setting the appropriate eviction policy and setting the timeout in Spring Session to avoid session keys without TTL (use 'spring.session.timeout' with 'spring.session.redis.cleanup-cron=0 * * * * *' to clean up the Redis cache. For the API gateway timeout, use KB-00018 to identify the slow upstream service by checking `upstream_response_time` in access logs and testing the direct call to the upstream service with 'curl -w '%{time_total}' -o /dev/null http://<upstream-host>:<port>/endpoint' to find if the issue is the upstream service itself or the gateway configuration timeout (increase proxy_read_timeout in Kong to 60000 if upstream is waiting on its own dependency and thread pool exhaustion if the upstream query is causing DB query regression or there's thread pool exhaustion). No direct escalate steps required from the given context.\", \"cited_artifacts\": [\"KB-00019\", \"KB-00018\"], \"confidence\": 0.5, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00026", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the Redis eviction and key expiry issue, use Spring Session with Redis eviction strategy 'allkeys-lru' by adding 'spring.session.redis.eviction-policy=allkeys-lru' to the application properties. For API gateway 504 timeout, monitor 'upstream_response_time' in the API gateway logs to find timeouts greater than 60 seconds which are common, and directly test the upstream service with curl -w '%{time_total}' -o /dev/null http://<upstream-host>:<port>/endpoint. If timeouts occur, increasing the KONG_PROXY_READ_TIMEOUT to 120 seconds could mitigate the issue. No direct escalation from the context, hence no cited_artifacts listed.\", \"cited_artifacts\": [\"KB-00019\", \"KB-00018\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 175}
{"ticket_id": "TRAIN-00026", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the Redis cache eviction issue, change the eviction policy to 'allkeys-lru' in Spring Session configuration by adding 'spring.session.redis.cleanup-cron=0 * * * * *' to enable cleanup and control eviction. For the API gateway 504 timeout issue, monitor the API gateway access logs to find recent 504 errors using `grep 504 /var/log/kong/access.log | awk '{print $7, $9}' | sort -n` to see if there's a trend in 504 timeouts from the same upstream service. No direct issues from the mentioned resources relate to the Kubernetes CrashLoopBackOff or Connection Pool exhaustion. Escalation tools are not applicable here. No concrete escalation needed.\", \"cited_artifacts\": [\"KB-00019\", \"KB-00018\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00046", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"There is no direct runbook for the integration of a manufacturing OT network segment using Modbus, DNP3 with BeyondCorp zero-trust access, which deals with the complexity of unifying OT with BeyondCorp’s agent and framework. However, we can suggest a workaround by managing access through BeyondCorp’s application control and policies for the OT devices and network. No specific runbooks or runbooks from the retrieved context directly apply to the integration of OT with BeyondCorp zero-trust access.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 175}
{"ticket_id": "TRAIN-00046", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For integrating the manufacturing OT network segment with BeyondCorp zero-trust access, there's no direct runbook mentioned in the provided context that aligns with the BeyondCorp framework or the OT network segment requirements. However, we can deduce that we need a solution involving the use of an agent or policy in BeyondCorp framework that can interface with devices using legacy protocols like Modbus and DNP3 without rewriting the agent. Unfortunately, no runbooks or step-by-step solutions are present in the provided context to address the integration of an OT network with BeyondCorp zero-trust access. Escalation to beyond the provided context is advised.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00046", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the BeyondCorp zero-trust access request, there are no direct runbooks or examples in the retrieved contexts that directly address the integration of an OT network segment that uses legacy protocols like Modbus and DNP3 into the BeyondCorp framework. The provided runbooks are for managing BeyondCorp agents and do not provide a way to integrate non-compliant OT devices with BeyondCorp. No specific runbooks for BeyondCorp and OT integration are available in the provided context. Escalation of the request is recommended due to the lack of direct solutions in the context.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00046", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the BeyondCorp zero-trust access request for the OT network segment, we can't find a direct runbook or context that addresses the exact needs for integrating the OT network with BeyondCorp’s zero-trust access framework due to the use of legacy protocols and BeyondCorp agent incompatibility. No specific runbooks or context points to a solution for this particular requirement from the provided retrieved contexts. Escalation to external knowledge is needed, and thus, we cannot provide a specific resolution without additional documentation on BeyondCorp agent compatibility or OT network integration practices. No definite runbook or context match found.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00046", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"There's no direct runbook in the retrieved context that directly addresses the need to onboard an OT network segment with legacy protocols like Modbus and DNP3 to the BeyondCorp zero-trust access framework. The context provided doesn't contain specific steps or runbooks for integrating an OT network into BeyondCorp. However, an indirect approach could be to look at BeyondCorp’s agent configuration to see if it supports adding protocols like Modbus or DNP3 through API or configuration modifications. No runbooks or direct steps are found for BeyondCorp zero-trust and OT network segmentation in the given context, hence no clear resolution can be provided.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00046", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"There are no direct runbooks or steps provided in the retrieved context that address the integration of an OT network segment using legacy protocols (Modbus, DNP3) into the BeyondCorp zero-trust access framework. The provided runbooks are not relevant to the requirement. No runbooks, diagnosis, or runbooks exist for the BeyondCorp zero-trust access requirement for an OT network segment with given protocols. Escalation of the ticket with the given data is not possible. No corresponding runbook exists in the context provided.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 175}
{"ticket_id": "TRAIN-00046", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"No relevant runbook or context exists within the provided retrieved information that can assist in onboarding an OT network segment using the BeyondCorp zero-trust access framework with legacy protocols (Modbus, DNP3). No runbooks or direct solutions are provided that can be derived from the given contexts to address the BeyondCorp zero-trust access for the OT network.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 175}
{"ticket_id": "TRAIN-00046", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"No direct runbook exists for onboarding an OT network segment with legacy protocols like Modbus and DNP3 to BeyondCorp zero-trust access, as the given runbooks deal with network and DNS issues. An OT agent for BeyondCorp could be developed in-house or sourced from a third-party vendor that specializes in OT network security and BeyondCorp integration. No direct references to an OT agent or beyond the given examples are provided in the retrieved context. Escalation to the IT department for further runbook creation or guidance from BeyondCorp for an OT agent runbook is recommended. There is no information in the provided contexts to resolve this ticket directly.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00032", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the heap usage issue, ensure that Guava Cache or Caffeine in Guava Cache are not unbounded by setting a maximumSize in both (e.g., `guava.cache.maximumSize = 10000`). For the Redis eviction issue, Redis eviction or key expiry needs to be configured to `allkeys-lru` with the eviction policy by setting `maxmemory_policy` to `allkeys-lru` with `redis-cli CONFIG SET maxmemory 700mb` and ensuring the maxmemory setting doesn't exceed 90% of available memory (`redis-cli CONFIG GET maxmemory`). No escalate needed as all required configurations are mentioned in the retrieved contexts. \", \"cited_artifacts\": [\"KB-00015\", \"KB-00019\"], \"confidence\": 0.1, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00032", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the heap memory exhaustion in the checkout-service, increase the heap memory by setting -Xmx6g in the application properties for Guava Cache and Caffeine maximumSize to avoid unbounded caches. For Redis cache issues, ensure maxmemory is set to avoid memory pressure by running 'redis-cli CONFIG SET maxmemory <memory>MB'. No direct root cause diagnosis for the Redis eviction policy issue in the given context. Ensure session keys are not unbounded and add 'spring.session.redis.cleanup-cron=0 * * * * *' to handle spring session cleanup. No direct fixes for the cache keys without TTL and no mention in the given context. No Kubernetes CrashLoopBackOff mention in the provided context. No rollback procedures provided for the context given. No relevant steps from the provided context for the F5 health monitor failure mentioned.\", \"cited_artifacts\": [\"KB-00015\", \"KB-00019\"], \"confidence\": 0.1, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00032", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For heap exhaustion in the JVM, add the following JVM flags to the application properties in application.properties: -XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/var/log/heapdumps/ -XX:+ExitOnOutOfMemoryError to automatically generate heap dumps and force restart in case of OOM. For Redis eviction issues, ensure the eviction policy is set to 'allkeys-lru' using redis-cli CONFIG SET maxmemory-policy allkeys-lru. Set the Spring Session timeout in the configuration to prevent sessions from staying forever by default with spring.session.timeout set to a specific time and using spring.session.redis.cleanup-cron=0 * * * * * for periodic cleanup. No direct resolution from the given context on Kubernetes CrashLoopBackOff or rollback issues. Escalation not applicable with the provided context.\", \"cited_artifacts\": [\"KB-00015\", \"KB-00019\", \"spring.session.redis.cleanup-cron=0 * * * * *\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00032", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the heap memory exhaustion, add JVM flags `-XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/var/log/heapdumps/ -XX:+ExitOnOutOfMemoryError` to force a clean restart. For Redis eviction issues, set the eviction policy to `allkeys-lru` with `redis-cli CONFIG SET maxmemory_policy allkeys-lru`. Spring Session timeout is set to 0 by default, so add `spring.session.redis.cleanup-cron=0 * * * * *` to enable cleanup. No direct fix is mentioned for the Redis cache eviction issues directly from the context. No escalations to Kubernetes or rollback steps are seen in the context either. Escalation cannot be ruled out from the given information.\", \"cited_artifacts\": [\"KB-00015\", \"KB-00019\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00032", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the heap memory issue, use the JVM heap dump analysis tool to capture the heap dump using the command: `jmap -dump:format=b,file=/tmp/heap_$(date +%s).hprof <pid>` and check the heap dump with Eclipse MAT (Steps 2-4 for heap analysis). For Redis eviction issues, use the Redis configuration to ensure eviction policy is correct: `redis-cli CONFIG GET maxmemory-policy` and set eviction to `allkeys-lru` with `redis-cli CONFIG SET maxmemory <target-memory>` if needed. No direct steps are found for Spring session timeout or Kubernetes crash loop in the provided context, so we will not proceed with those. Escalation is not required for this issue as all needed info is available.\", \"cited_artifacts\": [\"KB-00015\", \"KB-00019\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00032", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The heap memory issue in the checkout-service v2.2 can be mitigated by enabling the automatic heap dump on OOM via the `-XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/var/log/heapdumps/ -XX:+ExitOnOutOfMemoryError` JVM flags and capturing it with the `-XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/tmp/heap_$(date +%s).hprof` command, to be run on the process id (pid) of the heap usage. For the Redis cache eviction issue, use `redis-cli CONFIG SET maxmemory 700mb` to set memory limit and `redis-cli CONFIG SET maxmemory-policy allkeys-lru` to ensure the eviction policy is set to `allkeys-lru`. No specific steps from the provided context directly relate to the crashloopbackoff or rollback procedures, hence no artifacts listed for these. Escalation is not applicable here.\", \"cited_artifacts\": [\"KB-00015\", \"KB-00019\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00032", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For heap memory issue, add the JVM heap dump configuration with the given flags: `-XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/var/log/heapdumps/ -XX:+ExitOnOutOfMemoryError -XX:+HeapDumpFullThreshold -XX:HeapDumpFullThreshold=100` to the startup scripts of the checkout-service to analyze heap usage. For Redis eviction issues, switch to the `allkeys-lru` eviction policy with `redis-cli CONFIG SET maxmemory-policy allkeys-lru` and manage Redis memory with `redis-cli CONFIG SET maxmemory 600mb` to avoid memory overflow. No direct solutions from the given context for Redis cache eviction or Kubernetes issues.\", \"cited_artifacts\": [\"KB-00015\", \"KB-00019\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 175}
{"ticket_id": "TRAIN-00032", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the heap issue, add the JVM flags `-XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/var/log/heapdumps/ -XX:+ExitOnOutOfMemoryError` and capture heap dump using `jmap -dump:format=b,file=/tmp/heap_$(date +%s).hprof <pid>` on the process. For Redis eviction issue, set the Redis eviction policy to `allkeys-lru` using `redis-cli CONFIG SET maxmemory-policy allkeys-lru` to reduce memory pressure. For Spring Session forever caching in Guava Cache, add `guava.cache.maximumSize` to 100 in `application.properties`. No direct resolution for Redis cache eviction and session timeout configuration error from the provided contexts. No issues regarding rollback procedures for this specific service from given contexts. Escalation not applicable for these issues.\", \"cited_artifacts\": [\"KB-00015\", \"KB-00019\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00044", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the CUDA_ERROR_OUT_OF_MEMORY error on the A10G GPU in the GPU inference service, there is no direct runbook provided in the retrieved contexts. However, similar to an unbounded cache issue in Guava Cache leading to a memory bloat (as in KB-00015), the GPU memory exhaustion could be due to an unmanaged GPU cache that is not automatically freed, similar to Guava Cache being unbounded and hence bloating the heap memory. No direct methods from the retrieved contexts directly apply to GPU memory exhaustion. Escalation to the Heap Memory issue from the Guava Cache problem could suggest that for GPU inference service, we could try to manage GPU memory usage in a way Guava Cache does for heap memory, by setting a maximumSize limit for GPU buffer usage, which is not directly mentioned in the contexts provided. We need to refer to another area not covered by the provided contexts, but in terms of understanding, we can look to the suggestion of setting a 'maximumSize' for Guava Cache in the Heap Memory solution to find a possible workaround for GPU buffer size, if any such method exists. Escalation to direct GPU buffers' memory management is not resolved as there is no direct solution in the contexts.', 'cited_artifacts': [], 'confidence': 0.0, 'escalate': true}}\n```", "parsed": false, "r_parsimony": 0.0, "step": 175}
{"ticket_id": "TRAIN-00044", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the CUDA out-of-memory issue on the A10G GPU with the inference service, there is no direct runbook in the provided context. However, for managing GPU memory on a GPU, we can refer to the heap management issue from the spring session cache (KB-00015) as it pertains to managing memory leaks in Java applications. To manage GPU memory, consider monitoring the GPU usage with tools like NVIDIA Nsight Visual Studio Edition or NVIDIA CUDA Visual Profiler. No direct references to GPU memory usage are available in the given context. Therefore, we can't provide a concrete solution from the given sources for GPU out-of-memory issues, and escalate to a generic recommendation for memory leak in Java apps if needed.\", \"cited_artifacts\": [\"KB-00015\"], \"confidence\": 0.0, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00044", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the GPU inference service error on the A10G GPU hitting CUDA_ERROR_OUT_OF_MEMORY on the A10G GPU, there's no direct runbook from the provided contexts. However, for memory issues, we can draw a parallel from the given context for heap exhaustion in the JVM and the concept of allocation limit on CUDA. No direct suggestions from the provided context for GPU memory management like the GPU VRAM limit or CUDA tensor sizes, but we can infer that managing memory could be similar to preventing heap overflow in the JVM where 'maximumSize' config or setting a limit on allocation can be useful. We can suggest to increase the memory limit (similar to 'maximumSize') on the GPU for tensor or data allocation in the model. No previous runbook or past tickets mentioned for GPU inference specific VRAM management. Escalation to GPU driver settings or CUDA specific API to limit tensor sizes could be a research avenue, but we don't have specific runbooks or tickets from the given context to guide this.\", \"cited_artifacts\": [\"KB-00015\"], \"confidence\": 0.0, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00044", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the CUDA_ERROR_OUT_OF_MEMORY error on the A10G GPU in the image classification service, there is no direct runbook or precedent in the provided context. However, one might consider the spring session misconfiguration in the JMM out of memory error runbook as a workaround due to the session timeout default being set to forever which could be leading to memory bloat. No specific GPU memory management runbook was found in the provided context. Escalation to JVM heap management is not applicable as no issues with heap, connection pools, or cache eviction are mentioned.\", \"cited_artifacts\": [\"KB-00015\"], \"confidence\": 0.0, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00044", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the GPU inference service hitting CUDA_ERROR_OUT_OF_MEMORY on the A10G GPU, we can refer to the context on handling memory issues in machine learning (ML) inference. For memory management on GPUs, there is no direct reference in the provided contexts. However, for the GPU memory management, we can draw a parallel to the concept of heap exhaustion in the Java OOM cases. Hence, for the GPU memory issue, there is no direct runbook from the given contexts. Escalation to a common memory heap analysis in Java can be an analogy. No specific memory management runbook from the provided context for GPU. No actionable suggestions can be derived directly from the given contexts. Escalation to common runbooks could be a basis for solutions, leading to no direct resolution from the context.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00044", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the GPU out-of-memory error on the A10G GPU, there isn't a direct runbook from the provided contexts. However, for services that might be consuming GPU memory, increasing the GPU memory allocation through CUDA_VISIBLE_DEVICES or setting appropriate CUDA_MAX_MEMORY might be a workaround if directly configuring the GPU to boost VRAM isn't applicable. There's no specific context from the provided runbooks that can be directly used to manage GPU memory usage or VRAM outages. Escalation to a runbook-based solution isn't possible from the given details.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 175}
{"ticket_id": "TRAIN-00044", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the GPU inference service on the A10G GPU hitting CUDA_ERROR_OUT_OF_MEMORY with no precedent runbook, there is no direct support from the provided context. The situation described is related to memory exhaustion in a GPU, which isn't mentioned in the given context. We can infer that the CUDA error is an issue with the GPU’s VRAM which is not described or mentioned in any of the context, so we cannot provide a definitive solution from the given sources.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 175}
{"ticket_id": "TRAIN-00044", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the CUDA out-of-memory error on the A10G GPU for the image classification service, there isn't a direct runbook in the provided context that addresses the GPU memory management issue directly. However, we can look into GPU memory optimization techniques which aren't listed here. There's no concrete fix based on the provided knowledge base (KB-00019, KB-00015, KB-00016). Escalation is recommended to address this due to the lack of specific GPU memory management techniques in the provided contexts.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 175}
{"ticket_id": "TRAIN-00045", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For managing the QoS policy for the Viptela SD-WAN traffic class for the Zoom Rooms appliances, there is no direct runbook or reference in the provided context that directly mentions Viptela SD-WAN QoS policy management. However, given the lack of explicit runbooks, we can refer to the methodology for configuring QoS policies in SD-WAN gateways which might be akin to policies in Viptela. SD-WAN policies are usually managed through gateways and a centralized management system which might have some QoS similar to policies you'd configure in Viptela. No direct runbooks, yet no escalation from the provided context suggests this could be about gateways or similar tools which might correlate to Viptela SD-WAN. No specific runbooks or tutorials from Viptela found within the provided context to directly guide the setup of a Zoom Rooms QoS policy in Viptela.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00045", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For configuring a new traffic class and QoS policy for the Zoom Rooms appliances in Viptela SD-WAN, there is no direct runbook or specific guidance available from the provided context about Viptela SD-WAN traffic class and QoS policy management. No clear relevant IDs (KB-XXXXX) are given in the retrieved context. Escalation of the ticket by reference to Viptela SD-WAN runbooks or Viptela SD-WAN QoS policies cannot be recommended. More detailed runbooks or direct command guides from Viptela support for such settings need to be looked at from Viptela official documentation or customer runbooks.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00045", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For managing the QoS policy and traffic class for the Viptela SD-WAN with the Zoom Rooms appliances, there is no direct runbook or guide provided in the retrieved context that fits the requirement. However, we can infer a general approach by similarity to network settings in SD-WAN where QoS is managed similarly to traffic classes and policies (though the specifics do not align with the SD-WAN Viptela guide provided). For Viptela SD-WAN QoS policy management, one can refer to best practices for setting up traffic classes and QoS similar to general SD-WAN configurations which can be derived from Viptela’s interface settings and the traffic class setup in an SD-WAN network. No direct specific runbooks for Viptela SD-WAN are available in the given context, thus no direct runbook identification.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00045", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For creating a new traffic class and QoS policy in the Viptela SD-WAN for the Zoom Rooms appliances, there’s no direct runbook or specific advice in the retrieved context. However, for configuration or policy changes, it would be akin to creating a new QoS policy which might be similar to Viptela SD-WAN management practices but no explicit Viptela runbooks are listed. There’s no specific issue from the provided context that directly pertains to Viptela QoS policy management and configuration, so a direct resolution is not possible based on the given context. Escalation is necessary if further information or runbooks aren't present.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00045", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the Viptela SD-WAN traffic class and QoS policy creation for Zoom Rooms appliances, there is no direct runbook available in the provided context to manage Viptela SD-WAN QoS policies. There are no specific runbooks or artifacts mentioned that directly relate to creating a traffic class and QoS policy in Viptela SD-WAN. Escalation to Viptela SD-WAN SD-WAN runbook or policy creation is required but the given context does not provide sufficient guidance or runbooks for Viptela SD-WAN QoS policy management.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 175}
{"ticket_id": "TRAIN-00045", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For configuring a new traffic class and QoS policy for the Zoom Rooms appliances in the Viptela SD-WAN, there are no direct runbooks or guidelines mentioned in the provided context related to Viptela SD-WAN QoS policy management. No runbook or direct advice is available from the provided context to resolve the SD-WAN QoS policy need for the video conferencing traffic. An escalated approach may be required to find a workaround or refer to Viptela’s official Viptela SD-WAN runbooks or Viptela SD-WAN forums for detailed policy management and traffic class setting. Escalation of support beyond the context provided.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00045", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For managing QoS policy in Cisco Viptela SD-WAN, there isn't a direct runbook or ticket in the provided context that covers the traffic class and QoS policy management in Viptela SD-WAN. However, the context could be applied to troubleshoot network issues within SD-WAN settings such as ensuring connectivity and stable parameters in SD-WAN configuration, which might indirectly relate to the SD-WAN traffic class and QoS policy. No specific runbook is cited in the provided context, hence escalation to general network runbooks for traffic class and QoS management. Escalation to Viptela SD-WAN official runbook or Viptela SD-WAN support forums recommended. No direct tie to the SD-WAN policy management from the provided context.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00045", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the Viptela SD-WAN QoS policy management for the Zoom Rooms appliances, there's no direct runbook or runbook similar to Viptela QoS policy management from the provided context. There are runbooks for troubleshooting certain network issues such as Cisco SD-WAN QoS policy aspect could be managed via Viptela itself under policies and QoS settings but nothing about policies for Viptela SD-WAN traffic class. No direct reference in the given context. Escalation of this ticket requires reaching Viptela-specific runbooks which might not be covered. We cannot directly resolve this with the provided runbooks and context.\", \"cited_artifacts\": [], \"confidence\": 0.0, \"escalate\": true}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00006", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the site-to-site VPN tunnel that is dropping every 20-40 minutes due to MTU mismatch and phase 1 algorithm mismatch, use KB-00002 for MTU adjustment by ensuring the physical MTU of the interfaces matches and aligning the MTU between peers on both router interfaces (use `ip ospf mtu-ignore` on both if not already done). For the Phase 1 (IKE) mismatch, ensure both ends of the tunnel use the identical IKE proposal: encryption (AES-256), PRF (SHA-256), DH group (14 or 19), and lifetime (86400s). For NAT-T misalignment, ensure that NAT-T is enabled on both ends of the tunnel (UDP 4500) and that the NAT-T flag is present on both sides. No need to escalate or refer to specific VPN tunnel flapping issue for the cited context provided.\", \"cited_artifacts\": [\"KB-00002\", \"KB-00005\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00006", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the site-to-site VPN tunnel flapping issue to the Singapore branch office, use KB-00002 to align the MTU between the two interfaces on both routers and ensure the area flags are consistent. Use KB-00005 for the IPSEC tunnel flapping by ensuring the IKE proposal algorithm matches between both ends and adjusting the DPD timers to align with the expected latency (set `dpd 60 retry 5` on the tunnel). No direct reference to a BGP peer session or API gateway timeout issues are found in the provided context for these aspects.\", \"cited_artifacts\": [\"KB-00002\", \"KB-00005\"], \"confidence\": 0.4, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00006", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the site-to-site VPN tunnel dropping every 20-40 minutes with an MTU mismatch (OSPF) and possibly a Phase 1 (IKEv2) algorithm mismatch (VPN Tunnel Flapping) issue, align MTU between the interfaces by ensuring the physical MTU matches the interface MTU using `ip ospf mtu-ignore` on both interfaces. For the algorithm mismatch in Phase 1 of the IPSec tunnel, ensure both ends use identical IKE proposal details (encryption, PRF, DH group, and lifetime) by checking the peer's configuration with `debug crypto ikev2` and verifying the `encr-key-props` and `preshared-key` settings match the local settings on both routers. For the Phase 2 parameter mismatch, use `debug crypto ikev2` to capture the NOTIFY payload and ensure there are no NO_PROPOSAL_CHOSEN or TS_UNACCEPTABLE messages. Increase the DPD timers to `dpd 60 retry 5` to handle latency. No specific AS or peer configuration issue or gateway timeout issue is indicated in the context, so no further detailed排查is suggested. Escalation steps are not applicable as all issues can be resolved with given guidance from the retrieved contexts.\", \"cited_artifacts\": [\"KB-00002\", \"KB-00005\", \"KB-00001\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00006", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the site-to-site VPN tunnel dropping every 20-40 minutes, since we are dealing with an issue of MTU mismatch due to an area-type mismatch and authentication is not the primary issue (there's no mention of authentication errors and no MD5 configuration details in the provided context), we should adjust the MTU and ensure area flags alignment for area 0 (since typical site-to-site VPN uses area 0). Use `show ip ospf neighbor detail` to identify the state and ensure it’s not in INIT or 2-WAY state. For area type mismatch, check `show ip ospf neighbor detail` for area flags and ensure both routers agree on `area 0 st` for the area flag. No direct fix for area type mismatch is in the provided context, hence we focus on MTU by ensuring interfaces have matching MTU and avoid `ip ospf mtu-ignore` due to the lack of MTU mismatch configuration. No direct reference for phase1/2 mismatch or NAT-T configuration, hence we do not address those components. Escalation is ruled out due to lack of direct context solutions for these issues.\", \"cited_artifacts\": [\"KB-00002\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00006", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the site-to-site VPN tunnel that is dropping every 20-40 minutes with no traffic stats indicating BGP peer session issues and no mention of NAT-T mismatch (as per the given steps and context), the main issue appears to stem from the MTU mismatch for the peer 198.51.100.22. Use `show ip ospf neighbor` to check if the remote router is stuck in INIT or 2-WAY state and adjust the MTU by ensuring the physical MTU on both peers matches and using `ip ospf mtu-ignore` (if MTU is the issue) on both interfaces. No direct reference to Phase 1 or Phase 2 algorithm mismatches or DPD misconfiguration from the provided context; hence, we focus on MTU MTU for the first issue of state transitions, but for second issue, Cisco debug `crypto ikev2` could be considered for algorithm comparison without direct evidence from context, which is insufficient. Escalation to other steps from context is not clear cut from the given information.\", \"cited_artifacts\": [\"KB-00002\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00006", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the Site-to-site VPN to branch dropping every 20-40 minutes, the issue seems to be an MTU mismatch since the issue fits the criteria of 'ExStart/Exchange' stuck state for OSPF adjacencies. Ensure that the MTU between the peers matches by checking the MTU settings on both interfaces with `show ip ospf neighbor detail` and ensuring the intervals between hello and dead are matching with `show ip ospf neighbor detail`. Additionally, ensure there is no area-type mismatch as 'non-stub area' mismatch will cause adjacency issues by checking `show ip ospf neighbor detail` for area flags and ensuring both routers agree on stub/nssa flags using `show ip ospf neighbor detail`. No direct resolution for Phase 1 (IKE) or Phase 2 (IPSec) parameter mismatch or DPD misconfiguration can be found in the provided context. No information on BGP peer sessions or API gateway timeouts. Escalation of this issue requires cross-referencing with the given resources and an expert check for other factors which are not present here.\", \"cited_artifacts\": [\"KB-00002\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00006", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the site-to-site VPN tunnel issue, the issue appears to stem from the MTU mismatch and Phase 2 (IPSec) parameter mismatch. For the MTU issue, ensure both interfaces on the peer 198.51.100.22 have the same MTU by running `show ip ospf neighbor detail` and ensuring there is no inconsistency in the hello/dead interval between the hello/dead interval on both routers. For the Phase 2 parameter mismatch in Phase 1 (IKE), ensure both ends use identical IKE proposal parameters: encryption (AES-256), PRF (SHA-256), DH group (14 or 19), and lifetime (86400s). For the DPD timeout, increase DPD timers from the default to `dpd 60 retry 5` if high latency is causing it. No specific information for the BGP peer session drop or API gateway timeout is available from the provided contexts.\", \"cited_artifacts\": [\"KB-00002\", \"KB-00018\", \"KB-00005\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00006", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the site-to-site VPN tunnel flapping (flapping) issue, we need to look at the configuration on both ends of the IPSec connection to ensure no phase 1 or phase 2 algorithm mismatches by using the debug crypto ikev2 command to capture the NOTIFY payload and ensure there are no TS_UNACCEPTABLE or NO_PROPOSAL_CHOSEN errors. For MTU mismatch, ensure that the MTU value on the interfaces in the same VRF on both routers matches and set `ip ospf mtu-ignore` on both interfaces or align the MTU value with the peer's MTU on the router that is peer to the same interface. There are no clear BGP hold time or AS configuration matches in the provided context, so we cannot resolve this with the given data. Escalation to above context steps is not applicable.\", \"cited_artifacts\": [\"KB-00002\", \"KB-00005\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00025", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the CrashLoopBackOff issue in the orders-service pods, check if the memory limit in the ConfigMap (check `kubectl get cm <name> -o yaml` for ConfigMap injection errors) or memory leakage (increase the limits with `resources.limits.memory` in the Deployment YAML) is causing the OOMKilled event. For the database connection pool exhaustion in the fulfillment-service, check if the connection pool metrics show saturation with 'idle' connections holding over 30000ms (check `Prometheus` or `Grafana` metrics for `hikaricp_connections_active` and `hikaricp_connections_max`), and if there are long-running queries or unclosed ResultSets in the thread dumps (use `kill -3 <pid>` to see threads in `RUNNABLE` or `WAITING` state with JDBC/database stack frames). For connection timeouts between the services (a red herring in the given context), refer to the API Gateway timeout context for upstream dependency slowness or DB query regression. No direct fix is given for the API Gateway timeout in the provided context, and thus, no concrete advice can be given. No artifact from the provided context directly addresses the API Gateway timeout; hence, we must not escalate.\", \"cited_artifacts\": [\"KB-00016\", \"KB-00017\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00025", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the CrashLoopBackOff in the orders-service pods and connection pool exhaustion in the fulfillment-service related to the database connection pool, there's no direct direct resolution in the provided contexts. However, for the CrashLoopBackOff issue in the orders-service pod, refer to KB-00017 for guidance on the common root causes and potential fixes: increase 'resources.limits.memory' limit and check if there's a missing secret (check `kubectl get secret <name>` for missing secret and `kubectl get cm <name> -o yaml` for config error in secret or configmap). For the connection pool exhaustion in fulfillment-service (for database), refer to KB-00016 for details and potential fixes: check the Prometheus metrics for the pool exhaustion and ensure the pool size is under control without allowing the pool to be full. Escalation to specific configurations and logs for MTU and area mismatch from KB-00002 or upstream service timeouts from KB-00018 are not applicable here as they don't directly correlate to the provided issues.\", \"cited_artifacts\": [\"KB-00017\", \"KB-00016\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00025", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the CrashLoopBackOff in the orders-service pods, refer to KB-00017 which details the 'CrashLoopBackOff — Root Cause Diagnosis' and suggests increasing the memory limit for the secret (resources.limits.memory) or fixing the memory leak by looking at the ConfigMap for missing secret reference (kubectl get cm <name> -o yaml) and resolving Config error. For the connection pool exhaustion in the fulfillment-service (related to the connection pool exhaustion) refer to KB-00016 which recommends restarting the service to flush the connection pool (Immediate recovery) and ensuring the pool exhaustion isn't due to low pool max or long-running queries. No specific Kubernetes or MTU issues are mentioned directly, so no direct advice from KBs for these from the provided context. Escalation is not required from the retrieved context.\", \"cited_artifacts\": [\"KB-00017\", \"KB-00016\"], \"confidence\": 0.1, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00025", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the CrashLoopBackOff in the orders-service pod, increase the memory limit in the ConfigMap using the `kubectl get cm <name> -o yaml` command if the error is 'Error'. For the database connection pool exhaustion in the fulfillment-service, use the HikariCP config by setting `max` to a reasonable value and ensure the `min` is above a threshold to avoid saturation. No direct solutions from retrieved contexts for API Gateway timeout or area mismatch as they are separate from the issues at hand. Escalation not required for these as they are covered by specific diagnostics in the context provided.\", \"cited_artifacts\": [\"KB-00016\", \"KB-00017\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00025", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the CrashLoopBackOff issue in the orders-service pods (root cause: Memory limit set too low), increase the resources.limits.memory in the deployment YAML to accommodate the required memory. For the database connection pool exhaustion on the fulfillment-service, the connection pool exhaustion can be managed by checking Prometheus/Grafana metrics for 'hikaricp_connections_max' and 'hikaricp_connections_active'. If the pool is saturated, decrease the max connections in the hikaricp_connections_max setting in the config file and ensure the application logic does not hold non-critical connections in idle state. No direct fixes for the database timeout error or API Gateway timeout are found from the context.\", \"cited_artifacts\": [\"KB-00016\", \"KB-00017\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00025", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the CrashLoopBackOff issue, it seems to be related to the memory limit in the application YAML, causing it to be killed due to OOMKilled (Step 1 — Get the last crash logs and check events are useful for this). For the connection pool exhaustion issue, it looks like the connection pool is not being managed correctly and is being saturated. For the database connection issues, it might be due to the connection pool exhaustion. Therefore, ensure the `resources.limits.memory` in the application YAML is reasonable and that the connection pool size is being managed by Kubernetes appropriately (for CrashLoopBackOff), and for connection pool exhaustion, check that the max connections in the connection pool (configured in hikariCP properties) and the request timeout in the Prometheus metric are not causing saturation and exhaustion, respectively. No direct guidance from retrieved context for API Gateway timeout is available; need to refer to a different API troubleshooting guide for this.\", \"cited_artifacts\": [\"KB-00017\", \"KB-00016\"], \"confidence\": 0.1, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00025", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the CrashLoopBackOff in the orders-service pods, increase the memory limit in the ConfigMap or Secret to prevent OOMKilled. For the connection pool exhaustion in the fulfillment-service, check if the connection pool size (hikaricp_connections_max) is too high and the connections are not being released. No specific context links to the API gateway timeout issue in the given context. Escalation not needed for these issues. Monitor these fixes and use the provided KB-00016 and KB-00017 references for further diagnostics and monitoring for connection pool exhaustion and CrashLoopBackOff respectively. No direct KB reference for API gateway timeout other than it’s external to the services and a specific API monitoring setup would be required.\", \"cited_artifacts\": [\"KB-00016\", \"KB-00017\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00025", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the CrashLoopBackOff in the orders-service pods, check if the 'OOMKilled' message in the 'Events' section of `kubectl describe pod <pod>` points to a memory limit setting (resources.limits.memory too low) and increase or fix this to prevent the memory leak. For the connection pool exhaustion on the DB side, check if the 'hikaricp_connections_active' metric in Prometheus/Grafana indicates that the pool is holding too many connections that are not being released, and increase the `max` limit for the connection pool in the configuration. No specific details about the API Gateway timeout are provided, so we need to look further for the DB connection pool issue and the CrashLoopBackOff issue which can be covered by the provided KB-00016 and KB-00017. No reference from the API Gateway timeout troubleshooting for now.\", \"cited_artifacts\": [\"KB-00016\", \"KB-00017\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00005", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The issue with the F5 LTM marking the pool members as down due to the backend redirect to HTTPS by the nginx server is due to the health monitor settings mismatch between the F5 and the backend redirect. Ensure the F5 health monitor is set to use an HTTPS send: HEAD /health HTTP/1.1 and recv: 'OK' in the F5 GUI under Local Traffic > Pools > Health Monitors. For the backend redirect to work without disrupting the pool health, it needs to be configured to forward this health check string to the F5 correctly. Since there’s no direct fix for the backend redirect issue in the provided contexts, no direct fix from the mentioned contexts can be applied. Escalation to OKTA context isn't related, hence no citations from OKTA context available. The F5 monitoring requires adjustment of the health check settings and backend configuration must ensure the redirect response remains compatible with the F5 health check parameters. No direct problem from the Redis context is relatable. \", \"cited_artifacts\": [\"KB-00016\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00005", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The F5 LTM is marking the pool members as red due to a health monitor issue. Ensure the health monitor type used on the pool ('app-backend-pool') matches the backend service and the 'recv' string configured correctly. Use an HTTPS monitor with 'send: HEAD /health HTTP/1.1\\\\r\\\\nHost: myapp.corp.example.com\\\\r\\\\n' and ensure the 'recv: OK' is set in the F5 GUI under 'Local Traffic > Pools > Pool List'. There is no specific fix for the backend health check response text mismatch as mentioned, but ensure the health check response does not contain data from the backend server that the health monitor expects (like 301 to HTTPS). No other issues from the retrieved contexts are directly relevant to the app-backend-pool failure on the F5 LTM. Escalation not required.\", \"cited_artifacts\": [\"KB-00016\", \"KB-00004\"], \"confidence\": 0.1, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00005", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The issue with the F5 LTM marking pool members red for the app-backend-pool is due to the health monitor not correctly reflecting the backend health checks. Since the health checks on the F5 LTM should look for a specific response or receive string (e.g., 'OK' for 'HTTP 200' status and Host header matching the backend check), ensure the receive string for the health monitor matches the backend response. Use the F5 CLI or iControl REST to update the monitor configuration ensuring the receive string correctly aligns with the backend response, which should be 'OK' if it’s for a successful HTTP/1.1 response. There’s no mention of cache issues or database connection pool exhaustion in the provided contexts, so no references to KBs for cache eviction or DB connection pooling are applicable. Escalation methods or changes in Okta SCIM are irrelevant to the F5 LTM health check issue.\", \"cited_artifacts\": [\"KB-00004\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00005", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The F5 LTM health monitor issue is due to an incorrect receive string in the monitor that does not match the backend health check response text. Use the 'recv: 'OK'' to configure the receive string in the monitor to match the backend health check response. Ensure that the health check URL and method (HTTP/HTTPS) match the backend's health check requirements. No specific configurations from the retrieved contexts directly apply to Redis eviction or Okta SCIM setup. Escalation to troubleshooting is not needed here as we have a solution for the health monitor configuration.\", \"cited_artifacts\": [\"KB-00016\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00005", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the F5 LTM issue with app-backend-pool marked as red, check the health monitors by ensuring the send string matches the backend's health check response text and that the receive string (recv: 'OK') is configured correctly as per Step 3 of the F5 LTM Health Monitor Failures guide (KB-00004). No direct solution can be found for the backend team adding an HTTPS redirect to their Nginx config affecting the F5 health status from the provided context. Escalation of support not possible with the given information.\", \"cited_artifacts\": [\"KB-00004\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 175}
{"ticket_id": "TRAIN-00005", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The issue with the F5 LTM health monitor failing to recognize the backend servers as healthy (showing red) is due to the HTTP health monitor sending a GET / HTTP/1.1 request without the expected host header that the backend server responds with. Ensure the receive string in the F5 health monitor is set correctly to 'OK' and use the F5 LTM Health Monitor Failures guide by setting the receive string to 'OK' and verifying connectivity from F5 self-IP with 'curl -H 'Host: myapp.corp.example.com' http://<member-ip>:<port>/health'. For the backend application, ensure the backend service does not expect a 301 redirection to HTTPS from the F5 health check. No specific configuration for Redis eviction issues or Okta SCIM configuration are mentioned in the provided context, so these are not applicable. Escalation to other details is not possible with the current context.\", \"cited_artifacts\": [\"KB-00004\"], \"confidence\": 0.1, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00005", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The issue with the F5 pool members showing red for app-backend-pool is due to the incorrect health monitor configuration. Use the HTTP health monitor on the F5 LTM with a 'recv' string that the backend health check response body contains. 'recv: 'OK' would be suitable for a default backend status. No key misconfiguration, Okta related, or database connection pool exhaustion is evident from the provided contexts. Thus, the problem is specific to the health check misalignment of the F5 health monitor on the app-backend-pool and requires fixing the health check setup on the F5 LTM through its GUI under Local Traffic > Pools > Pool List. No direct support from the retrieved context for this issue arises, hence no escalation is necessary.\", \"cited_artifacts\": [\"KB-00004\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00005", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The issue with the F5 LTM marking pool members as 'down' (red) is due to the health check response text mismatch. For the health checks to pass, ensure the health monitor send response matches the receive string. Use an HTTPS monitor with `recv: 'OK'` in the F5 configuration. No escalatable configuration or cache issues are mentioned in the context, thus Spring Session or Okta SCIM setup are irrelevant here. No direct command fixes can be derived from the provided context regarding the health check issues. Escalation not required as no direct reference to DB connection pool exhaustion or Redis cache eviction issues.\", \"cited_artifacts\": [\"KB-00004\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00011", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the svc-backup-prod password expiration issue in the Veeam backup software, refer to KB-00012 for the service account password rotation procedure. Update the new password in CyberArk or HashiCorp Vault BEFORE changing it in AD with the command `Update-ADPassword <svc-account> -NewPassword (ConvertTo-SecureString '<new-password>' -AsPlainText -Force) -Reset`. Then, rotate the service account in AD with `Set-ADAccountPassword -Identity <svc-account> -NewPassword (ConvertTo-SecureString '<new-password>' -AsPlainText -Force) -Reset`. Ensure all dependent services are updated in order: first by the `Get-ADUser <svc-account> -Properties ServicePrincipalNames,Description` command to check for dependent services, and by updating them atomically in `sc config <service> password= <new-password>` and `IIS Application Pools` settings. No other tickets provided give direct information to resolve certificate or token renewal issues.\", \"cited_artifacts\": [\"KB-00012\"], \"confidence\": 0.1, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00011", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the svc-backup-prod password expiration issue, follow the Service Account Password Rotation Procedure from KB-00012 for the rotation of the svc-backup-prod account password. Use CyberArk / HashiCorp Vault to update the new password first by running `Set-ADAccountPassword -Identity svc-backup-prod -NewPassword (ConvertTo-SecureString 'new-password' -AsPlainText -Force) -Reset` in PowerShell. Next, rotate the password in AD with `Set-ADAccountPassword -Identity svc-backup-prod -NewPassword (ConvertTo-SecureString 'new-password' -AsPlainText -Force) -Reset` and ensure the dependencies are checked by `Get-ADUser svc-backup-prod -Properties ServicePrincipalNames,Description`. No escalation needed as the context provides the required steps. No need to reference any other tickets from the provided context.\", \"cited_artifacts\": [\"KB-00012\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00011", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the svc-backup-prod password expiration issue, follow the Service Account Password Rotation Procedure (KB-00012) to ensure the account is rotated every 90 days. Update the new password in CyberArk / HashiCorp Vault before changing it in AD: `Set-ADAccountPassword -Identity svc-backup-prod -NewPassword (ConvertTo-SecureString 'newpassword' -AsPlainText -Force) -Reset`. For dependent services, ensure these are updated in the same order: `Get-ADUser svc-backup-prod -Properties ServicePrincipalNames,Description` and check for service-dependent entries. No other steps from the provided context directly relate to this issue beyond the account rotation procedure. Escalation is not required as the steps are available.\", \"cited_artifacts\": [\"KB-00012\"], \"confidence\": 0.4, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00011", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the svc-backup-prod password expiry issue, the service account needs to be rotated according to the procedure provided in KB-00012. First, before rotating the password, check for dependencies with: 'Get-ADUser svc-backup-prod -Properties ServicePrincipalNames,Description' in PowerShell to identify the services. Update the new password in CyberArk / HashiCorp Vault first: 'vault kv put secret/svc/svc-backup-prod/secure-password value=<new-password>' and then rotate in AD with 'Set-ADAccountPassword -Identity svc-backup-prod -NewPassword (ConvertTo-SecureString <new-password> -AsPlainText -Force) -Reset'. For windows services, 'sc config svc-backup-prod password=<new-password>' followed by 'sc stop svc-backup-prod && sc start svc-backup-prod'. No other options are suggested by the provided context, so there is no need to escalate. No specific certificates or API token rotations are involved here, so those references are irrelevant for this issue. \", \"cited_artifacts\": [\"KB-00012\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00011", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the svc-backup-prod password expiration issue, follow the Service Account Password Rotation Procedure from KB-00012 which states to rotate the password in CyberArk Vault first and then in Active Directory. In CyberArk, update the password with the command `vault kv put secret/svc/svc-backup-prod password=<new_password>`. In CyberArk Vault, ensure the password gets updated atomically with CyberArk vault operations. In AD, use `vault kv get secret/svc/svc-backup-prod | ConvertTo-SecureString -AsPlainText | Set-ADAccountPassword -Identity svc-backup-prod -NewPassword` to rotate the password in CyberArk Vault and `Set-ADAccountPassword -Identity svc-backup-prod -NewPassword (ConvertTo-SecureString 'new-password' -AsPlainText -Force) -Reset` in AD. Use `Get-ADUser svc-backup-prod -Properties ServicePrincipalNames,Description` to identify dependent services. No need for escalation as the details are provided in the given context.\", \"cited_artifacts\": [\"KB-00012\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00011", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the svc-backup-prod password expiry issue, follow the Service Account Password Rotation Procedure from KB-00012. Before rotating, identify all dependencies using `Get-ADUser svc-backup-prod -Properties ServicePrincipalNames,Description` to list dependent services and check config files for service names. Update the new password in CyberArk / HashiCorp Vault with `vault kv put secret/svc/svc-backup-prod/password <new-password>` and ensure to reset in AD with `Set-ADAccountPassword -Identity svc-backup-prod -NewPassword (ConvertTo-SecureString <new-password> -AsPlainText -Force) -Reset`. Rotate in AD with `Set-ADAccountPassword -Identity svc-backup-prod -NewPassword (ConvertTo-SecureString <new-password> -AsPlainText -Force) -Reset` and update dependent services in order: Windows services with `sc config <service> password= <new-password>` restart, IIS Application Pools with IIS Manager, and Scheduled tasks with appropriate settings in Task Scheduler. No specific escalation steps are evident from the provided context.\", \"cited_artifacts\": [\"KB-00012\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00011", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the svc-backup-prod password expiration issue, follow the steps provided in KB-00012 for service account password rotation procedure to ensure it is done every 90 days without breaking dependent services: Update the password in CyberArk / HashiCorp Vault BEFORE changing in AD using `Set-ADAccountPassword -Identity svc-backup-prod -NewPassword (ConvertTo-SecureString 'new-password' -AsPlainText -Force) -Reset`. For dependent services, rotate in AD and ensure the new password is updated in order in the CyberArk vault before moving to step 3 of the procedure. No need for escalate as the steps from the retrieved context directly point to the service account password rotation. No direct links to the Veeam backup software or cron job fix from the provided context.\", \"cited_artifacts\": [\"KB-00012\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00011", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the svc-backup-prod password rotation issue, follow the Service Account Password Rotation Procedure from KB-00012. Prior to rotating the password, identify all dependent services by running `Get-ADUser svc-backup-prod -Properties ServicePrincipalNames,Description` and checking the description for a list of dependent services. Update the new password in CyberArk / HashiCorp Vault BEFORE changing in AD with `vault kv put secret/svc/svc-backup-prod/pass new_password=<new-password>` and ensuring to reset the password in CyberArk Vault before changing in AD with `Set-ADAccountPassword -Identity svc-backup-prod -NewPassword (ConvertTo-SecureString 'new-password' -AsPlainText -Force) -Reset`. Rotate the password in AD with `Set-ADAccountPassword -Identity svc-backup-prod -NewPassword (ConvertTo-SecureString 'new-password' -AsPlainText -Force) -Reset`. Ensure all dependent services are updated in the correct order: sc config <service> password=<new-password> and restart the service, IIS App Pool to <svc-backup-prod> Identity and configure the task scheduler with the new password as the credentials. No other artifacts cited from the provided content are applicable to the TLS certificate or API tokens issues.\", \"cited_artifacts\": [\"KB-00012\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00022", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the VPN tunnel flapping issue, the phase 1 parameter mismatch in the Phase 1 (IKE) is causing the flapping due to the difference in the encryption (AES-256), PRF (SHA-256), DH group (14 or 19), and lifetime (86400s) between the two ends. Use the debug crypto ikev2 on the Cisco or check the logs for the absence of the NAT-T flag when one end is behind NAT. For the DHCP pool exhaustion, use `show ip dhcp pool` to confirm that utilization is above 95%, then clear ip dhcp binding * to reconfigure the DHCP scope. The DHCP scope exhaustion can be addressed by reducing the lease time from 8 days to 1 day with `ip dhcp pool CORP_WIFI / lease 1`. No clear escalation steps from the given context.\", \"cited_artifacts\": [\"KB-00005\", \"KB-00006\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00022", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the VPN tunnel flapping, align the IKE proposal parameters between both ends in the Phase 1 and Phase 2 using the following steps: Step 1 — Capture the NOTIFY payload on both VPN gateway logs and ensure there is no mismatch in algorithm (encryption, PRF, DH group, lifetime) as it causes NO_PROPOSAL_CHOSEN. For the DHCP pool exhaustion, reduce the DHCP lease time from 8 days to 1 day using `ip dhcp pool CORP_WIFI / lease 1` to prevent clients from falling back to 169.254.x.x address and reassign some to a secondary pool or expand the scope to /23 or add a new pool to accommodate the increasing number of devices. No escalations needed as the provided contexts cover both issues.\", \"cited_artifacts\": [\"KB-00005\", \"KB-00006\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00022", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the VPN tunnel flapping issue, align the IKE Phase 1 parameter by ensuring that both ends use the same IKE proposal: encryption (AES-256), PRF (SHA-256), DH group (14 or 19), and lifetime (86400s). Use the command `debug crypto ikev2` on Cisco devices to capture the NOTIFY payload and ensure there is no ‘NO_PROPOSAL_CHOSEN’ or ‘TS_UNACCEPTABLE’ error. For the DHCP pool exhaustion, adjust the lease time to reduce the lease length from 8 days to 1 day using `ip dhcp pool CORP_WIFI / lease 1` in the DHCP pool settings. Avoid changing DHCP scope by adding a new secondary pool if the current DHCP scope is full, and ensure DHCP snooping is enabled to prevent rogue DHCP servers from consuming addresses. No escalation is needed as these solutions are provided in the retrieved contexts.\", \"cited_artifacts\": [\"KB-00005\", \"KB-00006\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00022", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the VPN tunnel flapping issue, use the steps in the 'VPN Tunnel Flapping — IPSec IKEv2 Troubleshooting' (KB-00005) to align the IKE proposal on both sides by verifying the encryption, PRF, DH group, and lifetime parameters match on both the Cisco routers. For the DHCP pool exhaustion at 94% utilization, use the steps in the 'DHCP Scope Exhaustion — Emergency Recovery' (KB-00006) to clear the DHCP binding to force an immediate pool exhaustion recovery or to expand the DHCP pool scope, reducing the lease time from 8 days to 1 day (A). No escalations are needed for these issues as all required steps are found in the given retrieved contexts.\", \"cited_artifacts\": [\"KB-00005\", \"KB-00006\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00022", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the VPN tunnel flapping issue, align the IKE proposal on both ends by ensuring the encryption (AES-256), PRF (SHA-256), DH group (14 or 19), and lifetime (86400s) match on both sides as per KB-00005 (Step 2: Algorithm comparison). For the DHCP pool exhaustion, reduce the DHCP lease time from 8 days to 1 day by running `ip dhcp pool CORP_WIFI / lease 1` or expand the scope to /23 by using `show ip dhcp pool` and `clear ip dhcp binding *` to make space, or add a new secondary pool by using `show ip dhcp pool` and `ip dhcp pool NEW_SUBNET / subnet 24`. No other context supports addressing the API Gateway timeout issues, hence leave that out for this resolution.\", \"cited_artifacts\": [\"KB-00005\", \"KB-00006\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00022", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the VPN tunnel flapping issue, ensure the hold-timer mismatch by running `show ip ospf neighbor detail` on both ends and checking if the hold timer is consistent between the local AS and the peer AS configuration (`neighbor <ip> timers <keepalive> <hold>` can be used to adjust the timer). For the DHCP pool exhaustion, reduce the DHCP lease time to 1 day with `ip dhcp pool CORP_WIFI / lease 1` to prevent scope exhaustion. No direct match for API Gateway 504 Timeout troubleshooting in the given contexts, hence no direct advice. Escalation not required as all solutions are covered in the provided contexts.\", \"cited_artifacts\": [\"KB-00005\", \"KB-00006\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00022", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the VPN tunnel flapping issue in the Bangkok branch office, align the IKE proposal on both Cisco devices for Phase 1 parameters by ensuring encryption (AES-256), PRF (SHA-256), DH group (14 or 19), and lifetime (86400s) match on both ends. Use the debug crypto ikev2 to capture the NOTIFY payload on both Cisco devices to confirm algorithm mismatch by looking for 'NO_PROPOSAL_CHOSEN' or 'TS_UNACCEPTABLE'. For DHCP pool exhaustion, ensure the DHCP lease time on the DHCP server pool (CORP_WIFI) is reduced from 8 days to 1 day with `ip dhcp pool CORP_WIFI / lease 1` to prevent DHCP exhaustion. No escalation needed as the context directly points to fixes for both issues.\", \"cited_artifacts\": [\"KB-00005\", \"KB-00006\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00022", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the VPN tunnel flapping, follow these steps: Use `debug crypto ikev2` on the Cisco device and check for the NOTIFY payload to ensure both sides are using identical IKE proposal parameters such as encryption (AES-256), PRF (SHA-256), DH group (14 or 19), and lifetime (86400s). For the DHCP pool exhaustion, use `show ip dhcp pool` to confirm utilization is above 95% and execute `clear ip dhcp binding *` with caution to avoid issues in maintenance window or use `clear ip dhcp binding <specific-ip>` for targeted stale entries. No references from the provided contexts can clarify the API gateway timeout issue and are not applicable to the given problems. Escalation is not necessary for the issues provided.\", \"cited_artifacts\": [\"KB-00005\", \"KB-00006\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00028", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the OOM crash in the recommendation-engine, enable heap dump monitoring by adding the following JVM flags to the application: `-XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/var/log/heapdumps/ -XX:+ExitOnOutOfMemoryError` to ensure heap dumps are saved and the JVM exits cleanly on OOM. For the high DB connection pool exhaustion, ensure the connection pool is not holding too many connections by monitoring Prometheus/Grafana metrics and ensuring `hikaricp_connections_max` in the `prometheus.yml` is set correctly and the `hikaricp_connections_active` is less than `hikaricp_connections_max` in the `hikari.properties` file for the pool configuration. No direct resolution from the provided contexts for pod crash loop or Okta SCIM provisioning setup failure, so no tools call for these are provided here. Escalation step not applicable as the contexts provide multiple direct fix points.\", \"cited_artifacts\": [\"KB-00015\", \"KB-00016\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00028", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the JVM Out of Memory (OOM) crash in the recommendation-engine, add the JVM flags `-XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/var/log/heapdumps/ -XX:+ExitOnOutOfMemoryError` to force a clean restart. For the high DB connection pool exhaustion, ensure the HikariCP configuration has a max of 45 as `max`, and ensure `poolMaxTotal` in the config does not exceed `max`. Monitor `hikaricp_connections_active` and `hikaricp_connections_max` metrics in Prometheus/Grafana to see if the pool is saturated. For Guava Cache issue, consider adding a `maximumSize` to the Guava Cache to prevent heap bloat. No other tools from the given context can directly resolve the Kubernetes crashloopbackoff issue, hence no direct resolution available from the provided context. Escalation not required.\", \"cited_artifacts\": [\"KB-00015\", \"KB-00016\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00028", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the JVM Out of Memory crash, add the following JVM options to manage heap size: `-XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/var/log/heapdumps/ -XX:HeapDumpFullThreshold=1 -XX:InitHeapSize=256m -XX:MaxHeapSize=512m`. For high DB connection pool exhaustion, ensure the connection pool is not overused by reducing the `max` value and tuning `maxWait` in the `hikari.properties`. No other tickets from the given context directly suggest a direct link between the JVM heap size, heap dumps, and DB connection pool exhaustion. Use the DB connection pool exhaustion guidance from KB-00016 for connection pools. Escalation to the Okta SCIM provisioning setup is irrelevant to the given problem. Monitor the OOM from the heap dump and connection pool exhaustion separately without any escalation steps needed.\", \"cited_artifacts\": [\"KB-00015\", \"KB-00016\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00028", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the JVM OOM crash on the recommendation-engine, configure the automatic heap dump with the given flags: `-XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/var/log/heapdumps/ -XX:+ExitOnOutOfMemoryError` to analyze the heap dump on the running process with 'jmap -dump:format=b,file=/tmp/heap_$(date +%s).hprof <pid>' and use Eclipse MAT for further analysis by checking 'Problem Suspect' with the largest retained heap and going to the dominator tree for large byte[] or char[] arrays. For DB connection pool exhaustion, use Prometheus/Grafana to check `hikaricp_connections_active` and `hikaricp_connections_max` to confirm pool exhaustion, and in thread dumps check `kill -3 <pid>` to see if there are threads in 'RUNNABLE' or 'WAITING' states with JDBC/database stack frames. No direct causes or resolutions for the DB connection pool exhaustion are provided in the context, so this might need additional troubleshooting which is not covered. No escalatory measures are applicable based on the provided information.\", \"cited_artifacts\": [\"KB-00015\", \"KB-00016\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00028", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the JVM Out of Memory (OOM) crashes in the recommendation-engine due to unbounded Guava Cache or Caffeine cache (common causes for heap exhaustion), add the JVM heap dump options: `-XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/var/log/heapdumps/ -XX:+ExitOnOutOfMemoryError` to the start-up script to force a clean restart. For the DB connection pool exhaustion, ensure the pool size and limit is adjusted by Prometheus/Grafana metrics to avoid saturation. For ModelCacheManager in the heap dumps analysis, use the heap dump with `-XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/var/log/heapdumps/` and analyze with Eclipse MAT. No direct issue from the retrieved contexts directly addresses the Kubernetes CrashLoopBackOff; thus, we must conclude there is no direct match for the Kubernetes issue from the provided context. No escalation indicated.\", \"cited_artifacts\": [\"KB-00015\", \"KB-00016\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00028", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the JVM Out of Memory crash due to unbounded cache (Guava Cache or Caffeine) issue, add the JVM flags `-XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/var/log/heapdumps/ -XX:+ExitOnOutOfMemoryError` and enable heap dump by running `jmap -dump:format=b,file=/tmp/heap_$(date +%s).hprof <pid>` to capture the heap dump on the running process and analyze it with the Eclipse MAT tool to look for the large byte[] or char[] indicating cache bloat. For the DB connection pool exhaustion, ensure the Prometheus/Grafana metric `hikaricp_connections_active` does not equal `hikaricp_connections_max` and check for `hikaricp_connections_pending` to confirm pool saturation. Use the command `kill -3 <pid>` to find threads in the application thread dump and look for long-running queries or unclosed ResultSets causing the issue. No direct ticket resolution link from the provided context for the connection pool exhaustion and Okta SCIM configuration. Escalation of context is not required since direct solutions are within the provided context but are separate tickets.\", \"cited_artifacts\": [\"KB-00015\", \"KB-00016\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00028", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the JVM heap exhaustion in the recommendation-engine, enable automatic heap dump by adding `-XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/var/log/heapdumps/ -XX:+ExitOnOutOfMemoryError` to the JVM startup parameters to ensure a heap dump is created on OOM. For DB connection pool exhaustion, increase the maximum number of active connections in the connection pool configuration in the application properties. For model cache bloat, Guava Cache needs to have a `maximumSize` configuration to avoid the Guava Cache being unbounded. No direct escalation from the provided context for Okta SCIM or Kubernetes issues.\", \"cited_artifacts\": [\"KB-00015\", \"KB-00016\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 175}
{"ticket_id": "TRAIN-00028", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the JVM Out of Memory (OOM) issue, add the JVM flags `-XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/var/log/heapdumps/ -XX:+ExitOnOutOfMemoryError` to avoid heap exhaustion and enable automatic heap dump in production. For the DB connection pool exhaustion, ensure the Prometheus/Grafana metric `hikaricp_connections_active` is not equal to `hikaricp_connections_max` and check `hikaricp_connections_pending`. For unbounded Guava Cache or Caffeine use (common in Guava Cache with `-XX:MaxHeapSize` not set), configure a maximumSize in the Cache to control its size. No direct tool for Okta SCIM configuration issues in this context is available, but follow the Okta SCIM attribute mapping steps as per the provided context to resolve the related Okta SCIM 2.0 provisioning failures. No tool calls can be made for Kubernetes crash loop with the given context.\", \"cited_artifacts\": [\"KB-00015\", \"KB-00016\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00001", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the BGP peer session down issue on CORE-02 with BGP peer 10.0.0.1 in the Active state, the most relevant solution is to ensure that the hold-timer mismatch for hold and keepalive intervals is corrected by using the `neighbor <ip> timers <keepalive> <hold>` command on the router, considering the `neighbor <ip> timers <keepalive> <hold>` or clear ACL blocking TCP/179 for `TCP connectivity reachability`. To validate configurations, ensure that the peer AS is configured correctly (neighbor <ip> remote-as <AS>) without any configuration mismatch. No specific fixes for hold-timer mismatch from the provided context but for AS and AS configuration mismatch from BGP peer session down guide in KB-00001. No information available for MTU or area mismatch from the provided context.\", \"cited_artifacts\": [\"KB-00001\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00001", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the BGP peer 10.0.0.1 in the Active state on CORE-02 router, the issue can be resolved by checking the hold-timer mismatch with the command `neighbor <ip> timers keepalive <hold>`. This aligns with Step 4 in the BGP Peer Session Down — Troubleshooting Guide (KB-00001). For AS number mismatch, ensure the local AS and peer AS numbers match with `neighbor <ip> remote-as <AS>` and for configuration mismatch, clear ACLs blocking TCP/179 on the router with `ping vrf MGMT <peer-ip> source <local-ip>`. There is no need to escalate to other steps as the solutions are explicitly provided in the BGP Peer Session Down guide and the hold-timer setting.\", \"cited_artifacts\": [\"KB-00001\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00001", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the BGP peer session down issue on CORE-02 to peer 10.0.0.1 (AS65100), the primary fix involves checking hold-timer mismatch and configuring the BGP hold time intervals correctly on both CORE-02. Use `show ip ospf neighbor` to identify if stuck in INIT state and check if there’s an MTU mismatch by checking `show ip ospf neighbor detail` for area flags. For BGP peers, `show ip ospf neighbor detail` should be used to check if the state is at 2-WAY or INIT, and for hold-timer mismatch, use `show ip ospf neighbors detail` and `show ip ospf neighbors detail` with `neighbor <ip> timers keepalive <hold>`. No direct fixes for peer state in BGP Active can be resolved from the provided context, however, the hold-timer mismatch in BGP peers can be managed by using `show ip ospf neighbors detail` for the state and `clear ip ospf adj` to clear adjacency states if necessary, but this isn't detailed in the provided contexts. No direct link to AS config or MD5 key mismatch from retrieved context. Hence, we focus on BGP hold-timer mismatch and MTU adjustment for CORE-02 to peer 10.0.0.1's connectivity. Escalation not needed from the provided context.\", \"cited_artifacts\": [\"KB-00001\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00001", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For CORE-02 BGP peer 10.0.0.1 session to be in Active state for 2 hours and not establishing, the hold-timer mismatch can be fixed by running the `show ip ospf neighbor detail` on the CORE-02 router to check for stuck states and fixing MTU discrepancies between interfaces. There’s no need to escalate to the BGP troubleshooting guide or Okta SCIM attributes as these focus on protocols and applications that are not relevant to the BGP peer issue between CORE-02 and peer 10.0.0.1. For MTU issues, ensure the MTU between CORE-02 interfaces aligns with peer 10.0.0.1, or add `ip ospf mtu-ignore` to both interfaces, or align the MTUs. No direct reference to BGP timer mismatch or hold timer mismatch for CORE-02 from the provided context.\", \"cited_artifacts\": [\"KB-00001\"], \"confidence\": 0.1, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00001", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For CORE-02, the BGP peer session to peer 10.0.0.1 is in an Active state and not establishing. The issue is a hold-timer mismatch with the peer. Use the command `show ip ospf neighbor detail` to see if it is stuck in any specific state (INIT, 2-WAY, EXSTART/EXCHANGE) and check for MTU or area-type mismatch using `show ip ospf neighbor detail` and `show ip ospf neighbor detail` respectively for adjacency issues. No need for troubleshooting other than the BGP specific steps from the BGP peer session down guide. No need to escalate to other specific zones or apps from other contexts as the context is limited to BGP and OSPF troubleshooting.\", \"cited_artifacts\": [\"KB-00001\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00001", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the BGP peer session down issue on CORE-02 with peer 10.0.0.1 in Active state and an AS configuration mismatch, correct this by ensuring the hold timers match between peers. Use the diagnostic steps from KB-00001, which suggests checking hold timer mismatch with the command `neighbor <ip> timers <keepalive> <hold>` or clearing ACLs blocking TCP/179 with `clear ip bgp neighbors detail`. No other steps from the context relate to MTU, area-type mismatch, or configuration issues for BGP peers that would require knowledge from KB-00002 or higher, hence we focus on BGP AS number match and hold timer mismatch. No need for escalation as all necessary steps are from the given context.\", \"cited_artifacts\": [\"KB-00001\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00001", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the BGP peer 10.0.0.1 in the Active state and no session establishing, we need to fix the hold timer mismatch which is described in KB-00001. We will use `show ip ospf neighbor` to identify if it’s in the INIT state which could imply the router is not receiving the hello from the peer. Since the problem here involves BGP peers, we use the BGP troubleshooting guide in KB-00001 and check the BGP hold timers with `show ip ospf neighbor detail | include hold time`. To clarify the AS numbers, we can confirm with `show ip ospf neighbors detail neighbor-id <peer-ip>`. For the configuration mismatch reason in KB-00001, ensure `neighbor <ip> timers <keepalive> <hold>` with `show ip ospf neighbor detail` to set the hold timer to the same as the peer. No other steps are clearly mentioned from the context so we won’t escalate the ticket for now.\", \"cited_artifacts\": [\"KB-00001\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00001", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the BGP peer session down issue on router CORE-02, the most appropriate troubleshooting approach is to follow the guide from KB-00001 which suggests verifying the hold-timer mismatch with `neighbor <ip> timers <keepalive> <hold>` or using `clear ip bgp <peer-ip>` to clear the ACL blocking TCP/179. No other steps mentioned in the retrieved contexts directly relate to the CORE-02 BGP peer issue. Escalation is not required.\", \"cited_artifacts\": [\"KB-00001\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 175}
{"ticket_id": "TRAIN-00050", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the rollback of the catalog-service v3.5.0 deployment with DB migration V38, you need to follow the Application Deployment Rollback Procedure from KB-00020 for the rollback of Kubernetes/Helm deployments. Use the command `helm rollback <release-name> 0` to rollback to the previous revision of the catalog-service. For the DB migration V38 rollback, there's no direct method provided in the given context but refer to the database migration rollback from Application Deployment Rollback Procedure where it states to check `/db/migrations/` for a corresponding `V<n>__down`. No specific rollback for DB migration rollback is provided in the provided context, so we might need to refer to the database migration rollback if such information is available but not directly mentioned here. No definite DB migration rollback strategy from provided context.\", \"cited_artifacts\": [\"KB-00020\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00050", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For rollback of catalog-service v3.5.0 DB migration V38 rollback, refer to the Application Deployment Rollback Procedure from KB-00020, which suggests using the Kubernetes/Helm rollback procedure with 'helm rollback <release-name> 0' or 'kubectl rollout undo deployment/<name>'. For DB migration rollback, check the history in the /db/migrations/ directory for a corresponding 'V<n>__down' migration. No direct KB references for DB migration rollback procedures but follow the Kube/Helm rollback steps for application rollback and check the DB migration directory for rollback steps if any. Escalation not required as specific DB migration rollback steps are not detailed in the given context.\", \"cited_artifacts\": [\"KB-00020\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00050", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For rollback of the catalog-service v3.5.0 deployment with DB migration V38 that added two tables, we need to follow the Application Deployment Rollback Procedure from KB-00020 which covers rollback for services on VMs using symlink-based deployment: Use 'ln -sfn /opt/app/releases/<prev-version> /opt/app/current' to rollback to the previous release and 'systemctl restart <service>' to restart the service. For database migration rollback, if the bad deployment ran a DB migration, there's no automatic rollback as per the context, so we need to check the directory '/db/migrations/' for a corresponding 'V<n>__down' file or script to do a manual migration rollback or revert based on the structure of the DB migration scripts if available. No direct DB migration rollback procedures like for DB migrations are described in the provided context, hence we would need supplementary information for that specific DB migration rollback process. Escalation not needed as no direct methods are outlined in the provided information for DB migration rollback.\", \"cited_artifacts\": [\"KB-00020\"], \"confidence\": 0.1, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00050", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For rollback of the catalog-service v3.5.0 DB migration V38 that added two tables, follow the Application Deployment Rollback Procedure in KB-00020 for the rollback of a bad deployment. Use the Kubernetes/Helm rollback steps: `helm rollback <release-name> 0` for the catalog-service release. For DB migration rollback, check if there's a corresponding `V<n>__down` in the `/db/migrations/` directory. No escalation is needed as the steps provided cover a rollback procedure for catalog-service v3.5.0 DB migration and rollback in Kubernetes/Helm. No specific details are given for legacy VM rollback for DB migration rollback.\", \"cited_artifacts\": [\"KB-00020\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00050", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the rollback of the catalog-service v3.5.0 deployment including DB migration V38, we can use the Application Deployment Rollback Procedure from KB-00020 to rollback the rollback within 15 minutes as a fast way for the Kubernetes/Helm deployments. For the DB migration rollback, there is no direct DB migration rollback procedure in the provided context, so we cannot perform the rollback of the DB migration directly and must seek further or external information for it. Hence, we suggest getting in touch with the DBA or following the general DBA intervention for rollback and watch out for any corresponding rollback migration from the DB migration schema. No specific DB migration rollback from the context provided and for DB migration rollback we need DBA or external procedures.\", \"cited_artifacts\": [\"KB-00020\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00050", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For rollback of the catalog-service v3.5.0 deployment with DB migration V38 rollback, follow the Application Deployment Rollback Procedure from KB-00020. Use the 'Kubernetes/Helm rollback (fastest)' method by running `helm rollback <release-name> 0` to roll back to the previous revision for the catalog-service. For the DB migration, as the context lacks specific rollback steps for DB migrations, you might need to explore the provided DBA intervention rollback for schema migration which doesn't provide a direct rollback path for DB migrations. Thus, we are unable to confidently escalate to a specific DB migration rollback procedure directly from the provided information.\", \"cited_artifacts\": [\"KB-00020\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00050", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For rollback of the DB migration V38 which added two tables in catalog-service v3.5.0, refer to the Application Deployment Rollback Procedure in KB-00020. For V38 migration rollback, use the Kubernetes/Helm rollback method: `helm rollback <release-name> 0` to revert to the previous revision of the catalog-service. Ensure the history check is done to verify rollback status: `helm history <release-name>` or `kubectl rollout history deployment/<name>`. There's no need for DB migration rollback procedure as it's mentioned under DB migration rollback, which suggests that the rollback of the bad deployment is related to the Kubernetes deployment rollback. No further escalation needed as the context is clear and mentions a rollback procedure under different scenarios.\", \"cited_artifacts\": [\"KB-00020\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00050", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For rollback of the catalog-service v3.5.0 with DB migration V38 that added two tables, use the Application Deployment Rollback Procedure from KB-00020 for rollback on a Kubernetes/Helm deployment. For DB migration rollback, check if a corresponding 'V<n>__down' script exists in '/db/migrations/'. No specific rollback steps are provided for DB migrations in the retrieved context, so we need to look for a DB migration rollback from the context or apply a generic rollback from history if possible. Given we can't directly correlate a DB migration rollback script, we might consider a general rollback of the catalog-service using the procedure for fast rollback in KB-00020 which applies to most services including Kubernetes/Helm deployments and legacy VM deployments. No direct DB migration rollback steps or framework are found in the provided context to apply directly for DB migration rollback. Escalation not needed as no clear rollback steps for DB migration are provided.\", \"cited_artifacts\": [\"KB-00020\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00029", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the IoT VLAN 60 DHCP pool exhaustion, follow the DHCP exhaustion steps from KB-00006: Clear the DHCP pool exhaustion by running `show ip dhcp pool` and if utilization > 95%, use `clear ip dhcp binding *` to refresh the DHCP scope and prevent stale leases. Use KB-00007 for the switch interface CRC error: Ensure the physical layer on the switch interface using `show interface <int> transceiver detail` checks the Rx power (below -20 dBm) and check cable length does not exceed 100m. No other steps are listed for the CRC error prevention from the provided contexts and are not directly applicable. Escalation is not required based on the contexts.\", \"cited_artifacts\": [\"KB-00006\", \"KB-00007\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00029", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the IoT VLAN 60 DHCP pool exhaustion, follow KB-00006's steps: `show ip dhcp pool` to confirm utilization and if >95%, use `clear ip dhcp binding *` to force all clients to renew in a maintenance window, or use `clear ip dhcp binding <specific-ip>` for targeted stale entries. For the interface CRC error on switch VLAN 60, use KB-00007's Step 2: check if there is duplex mismatch and fix it by setting both ends to `speed 1000 / duplex full`. No need to escalate to other KBs. Escalation of the VPN tunnel flapping issue is not relevant here.\", \"cited_artifacts\": [\"KB-00006\", \"KB-00007\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00029", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the IoT VLAN 60 DHCP pool exhaustion, refer to the DHCP Scope Exhaustion—Emergency Recovery (KB-00006) for immediate mitigation: use `clear ip dhcp binding *` to clear all DHCP bindings, which will force all clients to renew and reduce the pool utilization. For the interface CRC error on the switch, refer to the Interface CRC Error Investigation and Remediation (KB-00007) guide to solve: ensure the duplex setting on the interface is consistent with the remote peer's by running `show interface <int> | include duplex` and setting `speed 1000 / duplex full` for both interfaces. No escalatory steps from the provided contexts are applicable for the other contexts being unrelated to the issues at hand. Escalation is not applicable given the provided contexts.\", \"cited_artifacts\": [\"KB-00006\", \"KB-00007\"], \"confidence\": 0.4, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00029", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the IoT VLAN 60 DHCP pool exhaustion, use KB-00006 to reduce the DHCP lease time from 8 days to 1 day with the command `ip dhcp pool CORP_WIFI / lease 1`. For the CRC error rate on the switch uplink, check if there is a mismatch between the duplex settings on both ends by using `show interface <int> | include duplex` and `speed 1000 / duplex full` if needed. No direct reference in the context for the switch CRC error, so it can be left as is. Escalation tools are not applicable.\", \"cited_artifacts\": [\"KB-00006\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00029", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the IoT VLAN 60 DHCP pool exhaustion, refer to the mitigation steps in KB-00006 by running `show ip dhcp pool` to confirm utilization. If utilization > 95%, `clear ip dhcp binding *` to reset all lease records, which helps to reduce DHCP pool exhaustion. For the high CRC errors on the switch interface, follow the baseline counters check: `show interface <int> | include CRC|input|output` to get the CRC rate and check if it's above the threshold of 0.01%. If the rate is >1%, use the duplex mismatch check by `show interface <int> | include duplex` and ensure the speeds are set to both sides the same with `speed 1000 / duplex full`. No direct fix is provided for the interface error rate from the given context, but it can be managed by aligning the MTU between the peers if the CRC error is due to the MTU difference. No escalation is required as each step is discussed in the provided context.\", \"cited_artifacts\": [\"KB-00006\", \"KB-00007\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00029", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the IoT VLAN 60 DHCP pool exhaustion, use KB-00006 for the DHCP scope exhaustion remedy: execute `show ip dhcp pool` to check utilization; if >95% (like in KB-00006), use `clear ip dhcp binding *` to force clients to renew DHCP scope but in maintenance window; for the switch CRC error rate, use KB-00007 to check CRC input packets rate: use `show interface <int> | include CRC|input|output` to check the rate and recheck after a 5 minute wait. For the switch CRC error rate, check duplex mismatch: use `show interface <int> | include duplex` and fix with `speed 1000 / duplex full`. There is no direct reference for the access switch error in the provided context but we can troubleshoot CRC error rate based on available information.\", \"cited_artifacts\": [\"KB-00006\", \"KB-00007\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00029", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the DHCP pool exhaustion on IoT VLAN 60, use KB-00006 for the DHCP scope exhaustion immediate mitigation: run `show ip dhcp pool` to confirm utilization; if >95%, use `clear ip dhcp binding *` without the maintenance window risk. For the switch interface CRC error, use KB-00007 for the CRC error investigation and remedy: fix the duplex mismatch on the interface by ensuring both sides are set to `speed 1000 / duplex full` if not already. No explicit reference for the switch CRC error remedy in the provided context, but the general steps and KBs can guide the problem. Escalation: No information for escalation available in the provided context, thus no escalation tool call can be generated.\", \"cited_artifacts\": [\"KB-00006\", \"KB-00007\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00029", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the IoT VLAN 60 DHCP pool exhaustion, use KB-00006 to reduce the DHCP lease time from 8 days to 1 day using the command `ip dhcp pool CORP_WIFI / lease 1`. For the interface CRC errors, due to the mismatch in duplex settings, set both ends to full-duplex using `speed 1000 / duplex full` to ensure they are on the same speed and duplex. For the CRC error rate on the uplink to 3.1%, due to the duplex mismatch and not physical layer issues, set both ends to half-duplex or auto using `duplex half` on the most likely mismatched side. No direct reference for switch trunk configuration mismatch mentioned; check the interface for duplex mismatch and cable length for errors. No escalation needed as the tickets are specific to DHCP and interface settings with provided remedies.\", \"cited_artifacts\": [\"KB-00006\", \"KB-00007\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00037", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the branch office connection issues, we can resolve the problems with the following steps: For the VPN tunnel flapping: Ensure the algorithm mismatch between both sides by comparing the encryption (AES-256), PRF (SHA-256), DH group (14 or 19), and lifetime (86400s) on both routers by running `debug crypto ikev2` on Cisco. For the area mismatch in the OSPF adjacency failure, align the area flags by ensuring both routers agree on the area stub/nssa flags with `show ip ospf` and the configuration by setting area <id> st on the router. For the BGP peer session down, verify the hold-timer mismatch by `show ip ospf neighbor` and the configuration by `neighbor <ip> timers keepalive hold`. For the DNS resolution failure, ensure the correct internal forwarder chain by checking `dig @10.10.1.53 google.com +time=2` to find if there’s a block on the internal forwarder and by assigning the correct internal DNS server address in Windows by running `ipconfig /registerdns` and on Linux by running `sudo nano /etc/resolv.conf`. No specific ID from the SAML configuration error provided in the retrieved context. Escalation context not available from the provided information.\", \"cited_artifacts\": [\"KB-00002\", \"KB-00001\", \"KB-00003\", \"KB-00005\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00037", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the branch office connecting issues, align the MTU between the interfaces using the 'show ip ospf neighbor' command to check for INIT state and 'ip ospf mtu-ignore' for MTU issue. For the BGP peer session issue, ensure the hold-timer mismatch with `show ip ospf adj` for AREA-MISMATCH and `show ip ospf neighbors detail` to clear AUTHENTICATION conflicts. For the DNS resolution issue, ensure the internal zones are authoritative on the internal DNS server by verifying with `nslookup` and `dig @10.10.1.53` if external names use external resolvers. No specific SAML configuration provided. No need to escalate the issue based on the given context.\", \"cited_artifacts\": [\"KB-00002\", \"KB-00001\", \"KB-00003\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00037", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the issues with the new branch office (VPN tunnel flaps with NO_PROPOSAL_CHOSEN or TS_UNACCEPTABLE error, BGP peer session drop, and DNS resolution fail), the following steps can be taken: For the Phase 1 mismatch in the tunnel, ensure that the IKE proposal on both the branch and the corporate side are consistent: encryption (AES-256), PRF (SHA-256), DH group (14 or 19), and the lifetime (86400s). Use `debug crypto ikev2` on Cisco or check the logs for `NO_PROPOSAL_CHOSEN` error. For the MTU mismatch in the branch office network, ensure the MTU on the branch office interface equals that of the core router (aligning MTU and `ip ospf mtu-ignore` on both interfaces if mismatched). For the BGP peer session drop, the AS numbers and configuration need to match; use `show ip ospf neighbor` to check for area type mismatch and align stub/nssa flags in `area <id> st`. For the DNS resolution error, the branch office is using the wrong DNS server; ensure the correct internal DNS server is being used by checking `nslookup <hostname> 10.10.1.53` directly and resolving to the internal corp DNS. No SAML issues are listed in the given context, so that check is not applicable. Escalation and direct citation from given contexts for these issues.\", \"cited_artifacts\": [\"KB-00005\", \"KB-00002\", \"KB-00001\", \"KB-00003\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00037", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the branch office issue, the steps are as follows: For the Phase 1 tunnel issue, refer to KB-00005 where we align the IKE proposal on both the client and the gateway side by ensuring the same encryption (AES-256), PRF (SHA-256), DH group (14 or 19), and lifetime (86400s) parameters and ensuring there's no NAT-T mismatch between the client and the gateway. For the BGP peer session down issue, refer to KB-00001 where we correct the hold-timer mismatch by checking the 'keepalive' and 'hold' timers with `neighbor <ip> timers <keepalive> <hold>`. For BGP peer session down due to configuration mismatch, confirm that the AS numbers on both sides are the same and that MD5 passwords are identical. No specific issue related to SAML 2.0 for configuration from the provided contexts and no resolution applicable from DNS resolution as per KB-00003. No escalation or further search needed given the provided resources. Use `debug ip bgp <peer-ip> events` for the BGP peer session troubleshooting and `debug crypto ikev2` for the phase 1 tunnel issue. No specific configuration errors from SAML or authentication context.\", \"cited_artifacts\": [\"KB-00005\", \"KB-00001\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00037", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the Phase 1 tunnel flapping, use the steps for aligning the IKE proposal between the two endpoints by verifying identical IKE proposal: encryption (AES-256), PRF (SHA-256), DH group (14 or 19), and lifetime (86400s). For BGP peer session down, ensure there is no hold-timer mismatch by setting `neighbor <ip> timers <keepalive> <hold>`. For DNS resolution failures, ensure the correct internal DNS server is being used by checking the forwarder chain and zone delegation issues. No specific article from the retrieved context directly addresses the stuck states in OSPF adjacencies or the SAML 2.0 SSO configuration, so we rely on the above fixes. No escalate is needed for these issues based on the given context.\", \"cited_artifacts\": [\"KB-00003\", \"KB-00005\", \"KB-00002\", \"KB-00001\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00037", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the branch office issues, the VPN tunnel flaps are due to a mismatch in the IKE proposal between the two ends, which requires the alignment of the IKE proposal on both ends by ensuring encryption algorithm (AES-256), PRF (SHA-256), DH group (14 or 19), and lifetime match on both devices as per KB-00005 step 2 and 4. For the BGP peer session down issue, ensure that the hold-timer mismatch by using `neighbor <ip> timers <keepalive> <hold>` or clear ACL blocking TCP/179 on the router with the peer address, per KB-00001 step 1-4. For the BGP peer configuration issue, ensure the AS numbers are the same on both routers, and for SAML configuration errors, align the SP Entity ID, audience, ACS URL, and nameID format between the SP and IdP, as per KB-00003 step 1-4 and KB-00001 step 3. No specific configuration checks for the DNS resolution error in the provided context, which might mean the application uses the wrong DNS server. Use `nslookup <hostname> <dns-server-ip>` to check for the internal forwarder issues as per KB-00003 step 1-4. No specific escalation techniques from the provided context are applicable.\", \"cited_artifacts\": [\"KB-00005\", \"KB-00001\", \"KB-00003\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00037", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the issue with the VPN tunnel flapping (aligning the IKE proposal on both ends), the branch office BGP issue (correct hold-timer mismatch), and the DNS resolution problem (check forwarder chain and fix zone delegation issues), resolve the following: For the phase mismatch in the Phase 1 (IKEv2) tunnel with the Cisco CLI command `show crypto ikev2 sa` to check for the missing NAT-T flag and increase DPD timers as needed (Step 2 and Step 4 from the VPN Tunnel Flapping tool, Step 1 from the BGP Peer Session Down tool, and Step 3 from the Corporate DNS Resolution Failures tool). For the BGP peer sessions with the router command `show ip ospf neighbors detail` to identify area mismatch and `show ip ospf neighbors` to identify MTU mismatch. For DNS resolution, run `nslookup <hostname> <dns-server-ip>` and check `dig @10.10.1.53 google.com +time=2` to ensure the correct forwarders are used and the correct internal zones are authoritative. No specific ID from the context indicates an escalated ticket and thus no clear fix from the context provided.\", \"cited_artifacts\": [\"KB-00002\", \"KB-00001\", \"KB-00003\", \"KB-00005\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00037", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the IKE SA CHILD SA flapping issue, align the IKE proposal parameters on both ends ensuring encryption (AES-256), PRF (SHA-256), DH group (14 or 19), and lifetime (86400s) match. For BGP peer session down, ensure the hold timer interval matches both peer routers with `neighbor <ip> timers keepalive <interval> hold <interval>` and clear the ACL blocking TCP/179 with `clear ip bgp neighbors <peer-ip>` on Cisco router. For BGP peer session down and AS config, correct the AS numbers with `neighbor <ip> timers keepalive <interval> hold <interval>` and ensure `neighbor <ip> remote-as` config matches peer AS number. No direct fix for DNS resolution with the context given, though keep forwarder connectivity on 10.10.1.53 and ensure the internal zone delegation and DNS server assignment are correctly set in the client. No clear guidelines for SAML issues provided from the context.\", \"cited_artifacts\": [\"KB-00003\", \"KB-00002\", \"KB-00001\", \"KB-00005\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00018", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For rollback of the prod deployment of the billing-service, use the Kubernetes/Helm rollback procedure from KB-00020, which states to use `helm rollback <release-name> 0` to roll back to the previous version and `helm history <release-name>` to check the history and `kubectl rollout undo deployment/<name>` for rollback to the previous deployment if not using Helm. For a K8s rollback, `helm rollback <release-name> 0` can be used and it is fast. No need to escalate the rollback for this case. No additional resources from the provided contexts.\", \"cited_artifacts\": [\"KB-00020\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 175}
{"ticket_id": "TRAIN-00018", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For rolling back the prod deployment of the billing-service to version 2.7.0 using Helm, use the 'Application Deployment Rollback Procedure' from KB-00020, which states to use 'helm rollback <release-name> 0' to rollback to the previous revision. For example, if the release name is 'billing', use 'helm rollback billing 0'. For Kubernetes deployments, use 'kubectl rollout undo deployment/<name>' for non-Helm deployments. Monitor with 'kubectl rollout status deployment/<name> --timeout=120s' to ensure the rollback is complete and 'kubectl get pods' to check the status of the pods for readiness. No other steps are given from the other contexts for this rollback process. No escalations are needed as the context suffices for the rollback procedure.\", \"cited_artifacts\": [\"KB-00020\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00018", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the prod deployment rollback of the billing-service v2.8.0 which failed and needs to be rolled back to v2.7.0 within the Kubernetes/Helm rollback procedure, use the command `helm rollback <release-name> 0` to roll back to the previous version. In the Jenkinsfile, bind the credentials to the Helm release to ensure the rollback is done atomically. No need for manual rollback steps as the documentation provided is for fast rollback of a Kubernetes/Helm service. No need to rotate API tokens, JENkins API tokens, REST API tokens, or VM-based rollback steps due to the absence of direct guidance in the provided context. No escalations required since the procedure for rollback in Helm is clear and we have KB-00020 as the relevant reference.\", \"cited_artifacts\": [\"KB-00020\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00018", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the rollback of the prod deployment of the billing-service from v2.8.0 to the previous version within 15 minutes of the bad deployment according to the Application Deployment Rollback Procedure from KB-00020, use the Kubernetes/Helm rollback command `helm rollback <release-name> 0` to roll back to the previous revision of the billing-service. For non-Helm deployments, use `kubectl rollout undo deployment/<name>` to rollback. No need for service account rotation, API token rotation, database migration rollback, or TLS certificate renewal procedures. Ensure no downtime by checking the rollback with `helm history <release-name>` or `kubectl rollout history deployment/<name>` to ensure the service is rolled back without issues. Check the status with `kubectl rollout status deployment/<name> --timeout=120s` to see if the rollback was successful and to wait for pods to be ready. Use `kubectl get pods` for pod status and age to verify the rollback. Escalation is not required given the provided information.\", \"cited_artifacts\": [\"KB-00020\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00018", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the prod deployment rollback of the billing-service to v2.7.0, use the Application Deployment Rollback Procedure (KB-00020) for Kubernetes/Helm deployments. Use the 'helm rollback <release-name> 0' command to roll back to the previous version of the release. For non-Helm deployments, use 'kubectl rollout undo deployment/<name>'. Monitor the status with 'kubectl rollout status deployment/<name> --timeout=120s' for up to 2 mins to ensure the rollback completes successfully without breaking service. No escalation needed from the provided information.\", \"cited_artifacts\": [\"KB-00020\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 175}
{"ticket_id": "TRAIN-00018", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the rollback of the billing-service v2.8.0 deployment, follow the Kubernetes/Helm rollback procedure mentioned in KB-00020. Use the command 'helm rollback <release-name> 0' to roll back to the previous version of the release. For a non-Helm deployment, use 'kubectl rollout undo deployment/<name>'. Ensure that the rollback is done within the SLA limit of 15 minutes. No specific contexts from the provided retrieved information apply to API tokens, service account password rotation, or TLS certificate renewal as they are not relevant to the service rollback requested for the billing service deployment. No escalation techniques are suggested in the provided contexts, thus no escalate status or additional references are needed.\", \"cited_artifacts\": [\"KB-00020\"], \"confidence\": 0.1, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00018", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For rollback of the billing-service from version 2.8.0 to 2.7.0 using Helm, we can use the 'Application Deployment Rollback Procedure' from KB-00020. For this, you need to use the rollback command `helm rollback <release-name> 0` to roll back to the previous version. Ensure that the rollback is done within the limit of 15 minutes which is stipulated by the service rollback process in KB-00020. This is a fast rollback approach for services deployed with Helm and Kubernetes, with the command `helm rollback <release-name> 0` for the fastest rollback. No other steps directly from the provided context are needed for this rollback, as the exact rollback procedures are clearly mentioned for non-Helm and legacy VM scenarios in KB-00020 which are not applicable here.\", \"cited_artifacts\": [\"KB-00020\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00018", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For rollback of the prod deployment of the billing-service v2.8.0 to the previous version using Helm, follow the Application Deployment Rollback Procedure from KB-00020. Use the `helm rollback <release-name> 0` command for the fastest rollback. For Kubernetes/Helm deployments, use `helm rollback <release-name> 0` to roll back to the previous revision or use `kubectl rollout undo deployment/<name>` for non-Helm deployments if history is available. Check rollback status with `kubectl rollout status deployment/<name> --timeout=120s` for status and age verification. No need for zero-downtime rotation or credential rotation as these are not relevant to the service rollback. Escalation to direct ticket or further context is not applicable.\", \"cited_artifacts\": [\"KB-00020\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00014", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The high latency in the search service is due to the Redis eviction policy being set to 'allkeys-lru', which is causing cache misses and memory to exceed 95% usage. This increases database load and response latency. For immediate relief, we need to adjust the eviction policy to 'allkeys-random' which is less likely to cause cache misses and underutilizes memory. For the memory pressure issue, we need to add the spring-session configuration 'spring.session.redis.cleanup-cron=0 * * * * *' to flush the Spring Session forever cleanup cache and ensure no key stays forever in memory. No direct fix from the provided context relates to Kubernetes CrashLoopBackOff or API Gateway timeout issues, hence we will not escalate to these. Escalation is ruled out as the details provided are about the search service’s Redis memory usage and eviction.\", \"cited_artifacts\": [\"KB-00019\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00014", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the search-service high latency after Redis memory alert with 95% usage and eviction policy set to 'allkeys-lru', the Spring Session default forever session stores are the root cause due to session keys without a TTL set, leading to cache misses increasing database load. Fix this by ensuring that the Spring Session timeout is set to a finite value and adding a command to clean up sessions periodically (e.g., ‘spring.session.redis.cleanup-cron=0 * * * * *’ in the Spring application properties). For Redis memory usage, the eviction policy 'allkeys-lru' is correct and the usage of 95% is normal since ‘maxmemory’ is set to the total memory and maxmemory_policy is configured as ‘allkeys-lru’. No direct fixes are needed from the provided contexts for the Redis eviction or memory scaling as those do not pertain to the search service session and memory alert issue. Escalation is not needed since we have key advice from KB-00019 for the Spring session configuration and no other root cause from the context.\", \"cited_artifacts\": [\"KB-00019\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00014", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the search service response latency increase from 50ms to 800ms due to Redis memory alert at 95% with allkeys-lru eviction causing cache misses and OOM errors, we need to adjust Redis eviction and address session key misconfiguration. In Redis, use `redis-cli CONFIG SET maxmemory 70%` to adjust the maximum memory used to 70% of the maxmemory value. For session keys in Spring session, ensure `spring.session.redis.cleanup-cron` is set to remove session keys periodically to avoid memory overflow. There is no need to escalate as the context for the pod crash and DB timeout issues do not match the given problem. Escalation is not required.\", \"cited_artifacts\": [\"KB-00019\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00014", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the high latency in the search service due to Redis memory usage at 95% with allkeys-lru eviction causing cache misses and increasing database load, the immediate fix should involve scaling up Redis memory using `redis-cli CONFIG SET maxm` to increase the maximum memory Redis is allowed to use without eviction. For the Spring Session configuration causing keys with no TTL to fill memory indefinitely, ensure the `spring.session.redis.cleanup-cron` setting is in the application properties: Add `spring.session.redis.cleanup-cron=0 * * * * *`. Escalation of issues like Kubernetes CrashLoopBackOff or API Gateway timeout would not apply here as they do not align with the ticket details provided. No direct issues from the other retrieved contexts are pertinent to this situation.\", \"cited_artifacts\": [\"KB-00019\"], \"confidence\": 0.4, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00014", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The high latency of the search service is due to Redis using the allkeys-lru eviction policy, which leads to cache misses and increasing database load, contributing to the 800ms response time from 50ms. For the Redis eviction, the common misconfiguration is keys without time-to-live (TTL) on session keys. Add 'spring.session.redis.cleanup-cron=0 * * * * *' in the application properties to prevent the cache from filling indefinitely. No direct issue is found related to the Spring Session with Redis or the connection pool exhaustion. Escalation to common Kubernetes crashloopbackoff steps is not applicable here. Monitor the Redis maxmemory setting by using `redis-cli CONFIG GET maxmemory` to ensure it is not set too high. No direct information is available regarding API gateway timeout or Kubernetes crashloopbackoff steps from the provided context. Escalation to the provided context is not applicable as there are no steps given for API gateway timeout or the crash loop backoff problem from the provided context.\", \"cited_artifacts\": [\"KB-00019\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00014", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the search service high latency due to Redis memory usage with eviction policy set to allkeys-lru and 95% memory usage, you need to optimize the Redis eviction policy to `allkeys-random` for better cache performance and reduce cache misses. For the Spring Session issue causing cache misses by default storing sessions forever, you should add `spring.session.redis.cleanup-cron=0 * * * * *` in the configuration to ensure session data is cleaned up periodically. No context about the session timeout or K8s CrashLoopBackOff issues mentioned, so these do not apply here. No escalation needed based on the provided context.\", \"cited_artifacts\": [\"KB-00019\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00014", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the search service high latency due to Redis memory usage at 95% with allkeys-lru eviction causing cache hits and OOM misses, the Spring Session configuration should be adjusted to enable session cleanup. The configuration 'spring.session.redis.cleanup-cron=0 * * * * *' must be added to Spring to trigger cleanup at a regular interval. To address Redis eviction issues, ensure there are no keys without Time To Live (TTL) with 'spring.session.timeout' and monitor Redis eviction policy via 'redis-cli CONFIG GET maxmemory-policy' to be 'allkeys-lru' and tune Redis memory max using 'redis-cli CONFIG SET maxmemory <MB>' to prevent overloading. No direct solutions from other provided contexts apply to this issue.\", \"cited_artifacts\": [\"KB-00019\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00014", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the search-service high latency with Redis memory usage at 95% and eviction policy set to allkeys-lru, adjust the eviction policy to 'allkeys-random' with 'redis-cli CONFIG SET maxmemory-policy allkeys-random' to reduce cache hits and misses. For Spring Session configuration, add 'spring.session.redis.cleanup-cron=0 * * * * *' to avoid storing session keys forever which leads to memory exhaustion. No immediate changes to Redis memory need scaling as it is within acceptable limits and not related to connection pools or API gateway timeouts as mentioned in the context provided. Escalation of such a configuration issue to underlying pod issues or connection pool exhaustion is not related to the service latency with Redis. No such specific configuration mentioned in the context.\", \"cited_artifacts\": [\"KB-00019\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00017", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the connection pool exhaustion issue in the inventory-service leading to 'Connection is not available, request timed out after 30000ms' errors, you should use KB-00016 for diagnosing the connection pool exhaustion by checking Prometheus/Grafana metrics for hikaricp_connections_max and hikaricp_connections_active. For monitoring the database side, execute the query `SELECT count(*), state FROM pg_stat_activity GROUP BY state;` to monitor idle connection counts in PostgreSQL and check the status with `SHOW PROCESSLIST;` in MySQL to identify high idle and active connection counts. No need for API gateway timeout troubleshooting as it pertains to an external API service timeout (KB-00018). There is no information about Kubernetes Pod CrashLoopBackOff escalation mentioned in the context. Thus, no additional steps from the given contexts are required to resolve this issue.\", \"cited_artifacts\": [\"KB-00016\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00017", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the connection pool exhaustion issue in the inventory-service with 'Connection is not available, request timed out after 30000ms' error, increase the maximum number of connections in the HikariCP pool to prevent saturation by setting 'max' property to a suitable number in the configuration file under the HikariCP properties. Check the DB side by monitoring the active connections using the 'active' and 'idle' counts in the query 'SELECT count(*), state FROM pg_stat_activity GROUP BY state;' for PostgreSQL or 'SHOW PROCESSLIST;' for MySQL to ensure no idle connections are being held. No need for API gateway timeouts or Kubernetes issues based on the provided context. Escalation not required as the connection pool exhaustion is covered by KB-00016.\", \"cited_artifacts\": [\"KB-00016\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00017", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the connection pool exhaustion issue in the inventory-service, it is directly related to the Step 2 and Step 3 of the KB-00016 Troubleshooting Guide for Database Connection Pool Exhaustion. This step indicates that the pool is saturated and connections are being held by the application. Restart the application service to flush the pool by using the Prometheus/Grafana metric `hikaricp_connections_active` and check that it is not equal to `hikaricp_connections_max` to ensure the pool is not saturated. Additionally, check the thread dump for long-running threads in `RUNNABLE` or `WAITING` states in the application to find if there are unclosed ResultSets or long-running queries causing the exhaustion. No other steps from the retrieved context directly apply to the API Gateway timeout or Kubernetes pod issue. Escalation to the API Gateway timeout is not directly mentioned in the context provided and is distinct from the connection pool exhaustion issue in the DB.\", \"cited_artifacts\": [\"KB-00016\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00017", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the connection pool exhaustion error in the inventory-service, we need to check the database side for pool exhaustion. Use the Prometheus and Grafana metrics to monitor the pool status: `Prometheus - hikaricp_connections_active` and `hikaricp_connections_max` should not be equal, indicating the pool is not saturated. For the application thread dump, use `kill -3 <pid>` (Linux) and search for threads in `RUNNABLE` or `WAITING` states with JDBC/database stack frames for long-running queries or unclosed ResultSets. No need to refer to the other tickets as they are not relevant to the described issue.\", \"cited_artifacts\": [\"KB-00016\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00017", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the connection pool exhaustion issue in the inventory-service, follow these steps: Check the connection pool exhaustion in the Prometheus/Grafana metric `hikaricp_connections_active` and `hikaricp_connections_max` to ensure the pool is not saturated. Use the thread dump in Linux to identify if there are threads in `RUNNABLE` or `WAITING` state with JDBC/database stack frames, indicating a long-running query or an unclosed ResultSet. No references from the API gateway timeout issues or BGP peer session issues are relevant to the given problem. There are no references to Kubernetes Pod CrashLoopBackOff issues provided in the context. Escalation tools are not applicable here as the context directly correlates to the database connection pool exhaustion issue.\", \"cited_artifacts\": [\"KB-00016\"], \"confidence\": 0.4, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00017", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the inventory-service connection pool exhaustion issue, refer to the steps from the Diagnosis and Recovery for Connection Pool Exhaustion guide (KB-00016). Ensure that the connection pool (hikaricp_connections_max) is not reaching its limit and adjust the pool size if possible. Check for high 'idle' connections using Prometheus/Grafana metric 'hikaricp_connections_idle' and ensure 'hikaricp_connections_max' is not being exceeded. On the database side, ensure 'idle' connections are within acceptable limits without high numbers. No need to escalate as we have a direct step-by-step guide for the specific issue from KB-00016. No additional keys from other contexts apply to this ticket.\", \"cited_artifacts\": [\"KB-00016\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}
{"ticket_id": "TRAIN-00017", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the inventory-service connection pool exhaustion error, increase the pool size by configuring the HikariCP max parameter to avoid saturation (hikaricp_connections_max) and set the max idle timeout to reduce hanging idle connections (hikaricp_liveness_check\"). Use Prometheus and Grafana to monitor 'hikaricp_connections_max' and 'hikaricp_connections_pending'. No need to escalate as the context is about connection pool issues and does not include details about API gateway timeout or Kubernetes pod crash loops. \", \"cited_artifacts\": [\"KB-00016\"], \"confidence\": 0.3, \"escalate\": false}}\n```", "parsed": false, "r_parsimony": 0.0, "step": 175}
{"ticket_id": "TRAIN-00017", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the inventory-service connection pool exhaustion error, we need to adjust the pool size configuration in the application service. In the Prometheus/Grafana metric dashboard, we should check the 'hikaricp_connections_max' metric to ensure it is not too low. The thread dump analysis on Linux (using kill -3 <pid>) shows that long-running queries or unclosed ResultSets are common causes in the application. For the DB side, the pg_stat_activity query shows idle connection count indicating pool exhaustion. No need to refer to the API Gateway Timeout (KB-00018) as it is distinct from the inventory service issue and the Kubernetes crash loopback-off (KB-00017). No escalation required as we have the exact context for the issue. Monitor that the 'hikaricp_connections_max' metric is not too low to prevent pool exhaustion and check for long-running queries or unclosed ResultSets on the application side and idle connections on the DB side using the provided metrics and queries for early detection and resolution.\", \"cited_artifacts\": [\"KB-00016\"], \"confidence\": 0.2, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 175}