Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```


Response:
```json
{"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}}
```