Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "The notification-service pods are experiencing a CrashLoopBackOff after the new deployment. To diagnose, follow these steps:

**Step 1 — Get the last crash logs**
Use the command `kubectl logs <pod> --previous` to retrieve the logs from the previous container run, which often contains more detailed information about the issue.

**Step 2 — Check events**
Use the command `kubectl describe pod <pod>` to examine the Events section for common messages such as 'OOMKilled', 'Error', or 'CreateContainerConfigError'. This will help identify the root cause of the problem.

**Step 3 — Common root causes**
- **OOMKilled**: Increase the `resources.limits.memory` setting in the deployment configuration.
- **Missing secret**: Verify that the secret referenced in the ConfigMap exists in the correct namespace.
- **Config error**: Review the ConfigMap for any issues with environment variables or secret references.
- **Liveness probe too aggressive**: Adjust the liveness probe configuration if necessary.

**Step 4 — Kubernetes-specific considerations**
- Ensure that the new deployment includes the correct resources and that there are no changes in the pod definition or resource limits that could cause the pods to crash.
- Verify that the environment is consistent between staging and production. Differences in configurations or dependencies can cause unexpected behavior.

**Step 5 — Monitoring and Logging**
Monitor the system to ensure that the deployment is stable and that no new issues arise. Use Kubernetes monitoring tools to track pod status and resource usage.

After diagnosing and resolving the issue, perform a rolling deployment to ensure that the changes are applied correctly to all affected pods. Finally, verify the deployment by checking the logs and events again to confirm that the pods are running without issues.

If the problem persists, consider rolling back the deployment and reapplying the changes, ensuring to update all necessary configurations and dependencies.", "cited_artifacts": ["KB-00017"], "confidence": 0.9, "escalate": false}}
```


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "The CrashLoopBackOff issue with the notification-service pods after the deployment could be due to a resource limit issue or misconfiguration. Check the resource limits specified in the deployment YAML. If memory limits are too low, increase them. Also, ensure that there are no missing secrets or config errors in the deployment. Use kubectl describe pod <pod> to check the events for any error messages. If the issue persists, verify that the liveness probe is not too aggressive. For OOMKilled errors, ensure there are no memory leaks in the application. If the issue is related to SCIM provisioning or API token management, refer to the relevant KB articles for troubleshooting steps. If the problem persists despite these checks, escalate the issue to the development or ops team.", "cited_artifacts": ["KB-00017", "KB-00009"], "confidence": 0.85, "escalate": false}}
```


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "The notification-service pods are crashing due to a resource limit issue. The current deployment limits the memory resource too low, causing the containers to exit non-zero. To resolve this, increase the memory limit in the deployment configuration. Ensure the deployment YAML file specifies a higher memory limit than the current 2Gi. Additionally, check for any memory leaks in the application code. Verify the updated deployment, then manually scale down and scale up the affected pods to ensure the new configuration is applied correctly. If the memory limit still seems too low, consider increasing the overall resource limits for the namespace to ensure there is enough resources for all applications running in it.", "cited_artifacts": [], "confidence": 0.8, "escalate": false}}
```


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "The GitHub integration CI pipeline failing with HTTP 401 errors suggests an issue with the API token used by the github-integration-bot. Based on the context, this could be related to token rotation or expiration. To resolve this issue, follow these steps:

1. Generate a new API token in the GitHub Developer Settings.
2. Set the token's expiry to 90 days or less.
3. In the Jenkinsfile, update the credentials binding to use the new token.
4. Verify that all consumers of the token are using the new token by checking the Jenkins access logs.
5. After all consumers are using the new token, revoke the old token.

If the issue persists, check the GitHub Personal Access Token settings for any mismatches or incorrect configurations.

If the problem continues, escalate the issue to the IT team for further investigation into any upstream service timeouts or connectivity issues. If the CI pipeline is still failing, review the Jenkins logs and network access logs for any additional clues.

For future prevention, set up rotation reminders for these tokens and ensure that the CI pipeline is configured to check for and handle token revocation gracefully.", "cited_artifacts": ["KB-00013", "KB-00009"], "confidence": 0.85, "escalate": false}}
```


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "The issue with the GitHub integration CI pipeline failing due to a 401 HTTP response code for the github-integration-bot token can be resolved by revoking and rotating the token. Follow these steps:

1. **Generate a new token:** Navigate to the Developer Settings > Personal access tokens > Fine-grained tokens in the GitHub account. Generate a new token, set it to expire in 90 days, and apply a rotation reminder at -14 days.

2. **Update all consumers of the token:** In your Jenkins environment, update the credentials binding for the github-integration-bot token to use the new token. Ensure that the Jenkinsfile has the correct API token bound to the `github-integration-bot` user.

3. **Verify all consumers are using the new token:** Check the Jenkins access logs to confirm that the new token is being used for the GitHub API requests.

4. **Revoke the old token:** In GitHub Developer Settings, find the old token and revoke it.

5. **Push the new token to all consumers:** Ensure that the new token is pushed to all Jenkins jobs that use it.

6. **Test the pipeline:** After applying these changes, test the CI pipeline to ensure that it can successfully push build statuses to GitHub.

Make sure to review the context about token rotation and revocation in KB-00013 for more detailed instructions and best practices.

If the issue persists after following these steps, escalate the ticket.", "cited_artifacts": ["KB-00013"], "confidence": 0.85, "escalate": false}}
```


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "The 401 HTTP status code indicates an authentication issue with the API token used by the github-integration-bot. To resolve this, follow these steps:\n\n1. Confirm that the github-integration-bot's personal access token is still valid and has not expired.\n2. Check the token settings in the Jenkins configuration to ensure it is being used correctly.\n3. If the token is older than 90 days, consider generating a new token. Follow the instructions in KB-00013 for zero-downtime rotation.\n4. Update the Jenkinsfile to bind the new token.\n5. Verify that all consumers of the token are now using the new token by checking access logs.\n6. Revoke the old token to prevent unauthorized use.\n7. Test the CI pipeline to ensure it now functions correctly with the new token.\n\nIf the token is still valid but still causing issues, it may be due to the API gateway setting a timeout. Review the API Gateway access logs for 504 Gateway Timeout errors and test the upstream service directly to identify any performance issues. Adjust the timeout configuration in the API Gateway if necessary.",
 "cited_artifacts": ["KB-00013", "KB-00014"], "confidence": 0.85, "escalate": false}}
```


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "The GitHub integration CI pipeline failure due to the 401 HTTP status code for the github-integration-bot token suggests that the token might be either expired or revoked. Given that the token was working fine last month, it's likely that it has expired. To resolve this issue, you should revoke the current token and regenerate a new one. Here are the steps to revoke and regenerate the token:

1. Navigate to your Jenkins instance and go to Manage Jenkins > Configure System > API Tokens.
2. Locate the token for the github-integration-bot and click the 'Revise' button to revoke it.
3. Generate a new API token for the github-integration-bot.
4. Update the Jenkinsfile to use the newly generated token for pushing build status to GitHub.
5. Verify that the new token is being used by checking the Jenkins job logs and the GitHub API logs to ensure the 401 errors are resolved.
6. If the token issue persists, check for any potential network or upstream service issues as described in the KB-00018 troubleshooting guide for API Gateway 504 Timeout Troubleshooting.

Ensure that the token expiration is set to at least 90 days to avoid future issues. Set the token's expiry to -14 days as a reminder to regenerate the token when it nears expiration.

If the issue with the token is resolved, ensure that there are no other network issues that might be causing the 401 errors. Review the access logs for the GitHub integration and the upstream service (if applicable) to identify any other potential issues.

Cited artifacts: [KB-00013, KB-00018], Confidence: 0.85, Escalate: false", "cited_artifacts": ["KB-00013", "KB-00018"], "confidence": 0.85, "escalate": false}}
```


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "It appears that the GitHub Personal Access Token (PAT) used by the github-integration-bot to push build status to GitHub has expired or is no longer valid, causing the 401 HTTP status code. The GitHub PAT should be set to expire in 90 days and it's recommended to set rotation reminders to -14 days. To resolve this issue, please follow these steps:

1. Navigate to the Developer Settings in your GitHub account.
2. Go to Personal access tokens and generate a new fine-grained token with an expiry of 90 days or set a rotation reminder for -14 days.
3. Update the Jenkinsfile to use the newly generated token for the github-integration-bot. For example, if you are using a Bash script step in the Jenkinsfile to set the token, replace the existing token with the new one:
   ```yaml
   sh """
   export GITHUB_TOKEN=<new-generated-token>
   ```
4. Verify that the Jenkinsfile has been updated with the new token and save the changes.
5. Push the updated Jenkinsfile to the repository and trigger a build to test the new token.

If the issue persists, it might be worth checking the Jenkins API Token settings as well. Ensure that the token used in the Jenkinsfile matches the one configured in the Jenkins UI and that it has the necessary permissions to access the GitHub API.

Additionally, if the problem still exists, consider enabling and testing the SCIM provisioning to sync user lifecycle data from Okta to Jenkins. This might help identify any misconfigurations or issues with the SCIM integration. Please refer to KB-00018 for troubleshooting guidance on API Gateway 504 Timeout Troubleshooting if this does not resolve the issue.

To prevent future occurrences, set up email alerts for token rotation and regularly review and update tokens when necessary. You may also want to implement a webhook to notify your team of any token expiration or revocation events.

Cited Artifacts: [KB-00013, KB-00018, KB-00009]",
 "confidence": 0.85,
 "escalate": false}}
```


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "The GitHub integration CI pipeline failing with HTTP 401 indicates that the github-integration-bot token might have expired or been revoked. Follow the zero-downtime rotation pattern outlined in the KB-00013 article to rotate the token. First, generate a new token while the old one is still valid. Then, update all consumers of the token one by one or via a rolling deployment. Verify all consumers are using the new token by checking access logs. Finally, revoke the old token. Ensure that the new token has the appropriate permissions for the GitHub integration CI pipeline. Additionally, check the expiration settings for the GitHub Personal Access Token (PAT) in the Developer Settings > Personal access tokens. Set the expiration to 90 days and set rotation reminders at -14 days.", "cited_artifacts": ["KB-00013"], "confidence": 0.85, "escalate": false}}
```


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "The issue with the GitHub integration CI pipeline failing could be due to the API token used by the `github-integration-bot` expiring or being revoked. Please ensure the API token is renewed before it expires. Refer to KB-00013 for detailed steps on API token rotation and revocation. Additionally, check that the token has the correct permissions required by the Jenkinsfile and that it is correctly bound to the Jenkins user. Ensure that the Jenkins `github-api` plugin is up to date to support the SCIM provisioning. If the issue persists, investigate any changes in the downstream services or dependencies that might be causing timeouts, as mentioned in KB-00014 and KB-00018. Finally, check if the TLS certificate for the internal services is up to date, as mentioned in KB-00014, to avoid any TLS certificate-related issues affecting the pipeline. Please escalate if the problem continues after these steps are taken.", "cited_artifacts": ["KB-00013", "KB-00014", "KB-00018"], "confidence": 0.85, "escalate": false}}
```


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "It appears that the GitHub integration CI pipeline is failing due to a 401 HTTP status code. This might indicate that the API token used by the github-integration-bot has expired or been revoked. To resolve this issue, follow these steps:

1. Generate a new API token in the GitHub Developer settings > Personal access tokens > Fine-grained tokens.
2. Set the token's expiry to 90 days max per policy.
3. Set rotation reminders at -14 days.
4. Update the Jenkinsfile credentials binding to use the new token.
5. Verify all consumers are using the new token by checking Jenkins access logs.
6. Revoke the old API token to ensure there is no overlap.

Additionally, check the environment where the GitHub integration bot is running to ensure that it is using the correct credentials and that there are no network issues affecting the API call. If the issue persists, escalate the problem to the IT team as the problem might be related to the gateway or upstream services.

To further troubleshoot, you can review the access logs of the Jenkins and the GitHub integration bot to see any errors or failures related to the API call. Additionally, you can test the API call directly to the GitHub service to see if the token is indeed causing issues.

Please ensure to follow the zero-downtime rotation pattern as described in KB-00013 and KB-00018 to avoid any downtime during the token revocation process.

Please escalate the issue to the IT team if the problem persists after trying these steps and if further troubleshooting is required.", "cited_artifacts": ["KB-00013", "KB-00018"], "confidence": 0.85, "escalate": false}}
```


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "After the datacenter fiber maintenance, it's crucial to systematically investigate and address the network issues affecting BGP, OSPF, and DHCP. Here’s a comprehensive plan to resolve the issue:

1. **BGP Issues:**
   - Verify TCP/179 reachability: Use `ping vrf MGMT <peer-ip> source <local-ip>` from the router to the peer. If ping fails, check ACLs on both ends.
   - Check BGP state: Use `show bgp neighbors <peer-ip>` to identify 'BGP state = Active' or 'Idle (Admin)' states and ensure proper hold times and keepalive intervals.
   - Validate AS and peer configuration: Ensure `neighbor <ip> remote-as <AS>` on your router matches the peer's local AS. MD5 password mismatch causes BGP notifications.

2. **OSPF Issues:**
   - Identify stuck state: Use `show ip ospf neighbor` to check for stuck states like INIT or 2-WAY.
   - MTU mismatch: Use `show interfaces <int> | include mtu` to align MTUs between the interfaces on both sides.
   - Area-type mismatch: Check `show ip ospf` for area flags to ensure non-stub areas agree on the same configuration.
   - Authentication mismatch: Use `debug ip ospf adj` and look for 'Invalid authentication' messages to identify MD5 key issues.

3. **DHCP Issues:**
   - DHCP Scope Exhaustion: If utilization exceeds 95%, use `show ip dhcp pool` to confirm and clear binding entries: `clear ip dhcp binding *`. Identify and clear specific stale leases if necessary.
   - Identify and fix stale leases: Use `show ip dhcp binding | include Expiry` to find leases with expiry >7 days. Cross-reference with the ARP table for orphaned entries.

4. **DNS Issues:**
   - Confirm DNS resolution: Use `nslookup <hostname> <dns-server-ip>` to isolate DNS vs network issues. Check forwarder chain and ensure proper resolution from internal servers.
   - Clear DNS cache: On Windows: `ipconfig /flushdns`. On Linux: `sudo ip restart`.

5. **Physical Layer and VLAN Trunk Configurations:**
   - Check physical layer conditions: Use `show interfaces <int> transceiver detail` to check SFP power levels and reseat any faulty S


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "Based on the retrieved context, the issues you're experiencing appear to be related to network connectivity and configuration problems post-fiber maintenance. Here's a step-by-step resolution for the three issues:

1. **BGP Session Drops**: Perform a TCP reachability test to ensure that the BGP session isn't being blocked by ACLs. Use the following command to ping the BGP peer from the router:
   ```
   ping vrf MGMT <peer-ip> source <local-ip>
   ```
   Check the BGP state to confirm the session is down:
   ```
   show bgp neighbors <peer-ip>
   ```
   Correct any configuration mismatch such as AS numbers or MD5 passwords:
   ```
   neighbor <peer-ip> remote-as <correct-AS>
   neighbor <peer-ip> password <correct-MD5-password>
   ```

2. **OSPF Neighbor Issues**: Investigate for stuck states and mismatched parameters. Check the OSPF state to find where the adjacency is failing:
   ```
   show ip ospf neighbor
   ```
   Ensure MTU compatibility and authentication settings are correct:
   ```
   ip ospf mtu-ignore
   debug ip ospf adj
   ```

3. **DHCP Scope Exhaustion**: Identify the reason for the DHCP pool exhaustion. First, confirm if there are stale leases:
   ```
   show ip dhcp pool
   show ip dhcp binding | include Expiry
   show ip arp | include <subnet>
   ```
   Resolve the exhaustion by either reducing lease time, expanding the scope, or enabling DHCP snooping.

Review the logs to find any errors or notifications related to BGP, OSPF, and DHCP, and correct them accordingly. Additionally, ensure that the physical interfaces and VLAN configurations are correctly set up to avoid native VLAN mismatch issues.

Lastly, after making these changes, flush the DNS cache and perform a DNS resolution test to ensure DNS is functioning correctly. If the issues persist, retest all configurations and perform a full network sweep to identify any other potential connectivity issues.

To mitigate the impact of downtime, monitor the network closely and be prepared to perform additional troubleshooting steps as necessary.

Ensure all configurations are saved and backed up before making any changes to prevent potential rollback issues.

Consider reaching out to network administrators or contractors if the issue persists, as they may have additional insights or the tools needed to resolve the issue.",
    "cited_artifacts": ["KB-0


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "Given the multiple network failures reported after datacenter fiber maintenance, it's important to systematically investigate the root causes of BGP, OSPF, and DHCP issues. Here's a step-by-step approach for each:

1. **BGP Failure**: Verify TCP/179 reachability between the affected routers and the ISP. Check BGP state and configuration details to ensure there are no hold timer mismatches. Use `show bgp neighbors <peer-ip>` to verify the BGP state and `debug ip bgp <peer-ip> events` for notifications. If TCP/179 is blocked by ACLs, clear them. Correct hold timer settings with `neighbor <ip> timers <keepalive> <hold>` or clear ACLs blocking TCP 179. After fixing, run `clear ip bgp <peer-ip> events`.

2. **OSPF Failure**: Check OSPF neighbor adjacency between the core and distribution segments. Use `show ip ospf neighbor` to identify any stuck states (e.g., INIT, 2-WAY). For stuck INIT states, check the subnet mask and hello/dead intervals to ensure they match. Address MTU mismatch with `ip ospf mtu-ignore` on both interfaces or align MTUs. Resolve area-type mismatch by ensuring both routers agree on the stub/nssa flags in the `area <id>` statement. For authentication issues, ensure MD5 key mismatches are resolved.

3. **DHCP Pool Exhaustion**: Mitigate DHCP pool exhaustion alerts by identifying and clearing expired or stale leases. Use `show ip dhcp pool` to confirm utilization. If utilization is high (>95%), clear all DHCP bindings with `clear ip dhcp binding *`. For specific entries, use `clear ip dhcp binding <specific-ip>`. Implement preventative measures by reducing lease time or expanding the scope.

For physical layer issues, such as CRC errors, check for duplex mismatch and physical layer health. Use `show interface <int> | include CRC|input|output` to baseline counters and check duplex settings. Re-seat SFP modules for SFP interfaces and ensure cable length is within acceptable limits (e.g., Cat5e max 100m at 1G).

Once these issues are resolved, ensure DNS server configurations are correct and that the correct DNS servers are being used. Verify the forwarder chain and check for zone delegation issues. Clean the DNS cache on client machines with


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "The multiple network failures after the datacenter fiber maintenance can be attributed to several issues stemming from the BGP, OSPF, and DHCP. Below is a step-by-step approach to identify and resolve these issues:

1. **BGP Session Issues:** BGP session drops can occur due to TCP reachability issues, hold-timer expiry, or configuration mismatch. To verify TCP reachability, use the `ping vrf MGMT <peer-ip> source <local-ip>` command from the router VRF to the peer IP address. If the ping fails, check the access control lists (ACLs) on both ends for blocking TCP/179. For the BGP state, use `show bgp neighbors <peer-ip>` to check for 'BGP state = Active' (indicating a connectivity issue) or 'Idle (Admin)' (indicating a local configuration issue). The hold time and keepalive interval should be confirmed as well.

2. **OSPF Adjacency Issues:** OSPF adjacency failures may be due to MTU mismatch, area-type mismatch, or authentication issues. Check the subnet mask and hello/dead intervals to align them. MTU mismatches can be resolved by either aligning the physical MTUs or using `ip ospf mtu-ignore` on both interfaces. For area-type mismatches, ensure both routers agree on the stub/nssa flags in `area <id> st` for the respective areas. For authentication issues, use `debug ip ospf adj` to look for `Invalid authentication` messages and ensure MD5 keys match.

3. **DHCP Scope Exhaustion:** If a DHCP scope is showing exhaustion alerts, first check the utilization with `show ip dhcp pool`. If it's >95%, clear the DHCP bindings with `clear ip dhcp binding *` within the maintenance window to force a renewal, or clear specific bindings with `clear ip dhcp binding <specific-ip>`. Identify stale leases with `show ip dhcp binding | include Expiry` and cross-reference with the ARP table to find orphaned entries. For permanent fixes, reduce the lease time or expand the scope if necessary, and enable DHCP snooping to prevent rogue DHCP servers.

4. **DNS Issues:** Ensure that DNS resolution failures are not causing network errors. Isolate DNS vs. network with `nslookup <hostname> <dns-server-ip>`. If the external resolution fails but internal succeeds, check forwarder connectivity


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "Based on the issues described, it appears that the network failures after the datacenter fiber maintenance are related to BGP, OSPF, and DHCP issues. The first step should be to verify the BGP peer session status and reachability. Use the following commands to check the BGP status and verify TCP/179 reachability:

1. Verify TCP reachability: `ping vrf MGMT <peer-ip> source <local-ip>` from the router VRF to the ISP peer IP.
2. Check BGP state: `show bgp neighbors <peer-ip>` to check if the BGP session is active or idle.
3. Validate AS and peer configuration: Ensure that the BGP neighbor configuration matches between the two routers (including AS numbers and MD5 passwords).

For OSPF, since adjacency is not forming, check for MTU mismatches and other configuration issues. Ensure that the OSPF areas and configurations are correct, and the `ip ospf mtu-ignore` command is not being used inadvertently.

For DHCP, check for exhaustion and stale lease issues. Use the following commands to monitor and mitigate DHCP exhaustion:
1. Check utilization: `show ip dhcp pool` to confirm if the pool is fully utilized.
2. Clear bindings: `clear ip dhcp binding *` to force clients to re-acquire leases.
3. Identify and clear stale leases: `show ip dhcp binding | include Expiry` to find entries with expiry >7 days, and clear them with `clear ip dhcp binding <specific-ip>`.
4. Permanent fix options: Reduce lease time, expand the scope, or enable DHCP snooping.

Additionally, flush the DNS resolver cache on clients: `ipconfig /flushdns` on Windows and `sudo ipcache flush` on Linux.

Ensure physical layer and VLAN trunk configurations are correctly set. Reseat SFP modules and check cable lengths. For native VLAN mismatch, ensure the native VLAN is consistent between interfaces.

The immediate next steps involve fixing BGP and OSPF issues, monitoring DHCP for exhaustion, and flushing DNS. If issues persist, escalate to a network administrator or follow up with additional diagnostic steps based on the root causes identified.

To summarize, the solution involves verifying TCP/179 reachability for BGP peers, checking and configuring OSPF, mitigating DHCP exhaustion, and troubleshooting physical layer issues as outlined above.", "cited_artifacts": ["KB-00001",


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "The issues observed after the datacenter fiber maintenance are multifaceted, and they need to be addressed in a systematic manner. First, let's investigate the BGP session issues. Ensure that there is TCP connectivity to the ISP. Run the following commands to check reachability:

1. Ping the ISP's peer address from the router VRF to verify connectivity.
   ```sh
   ping vrf MGMT <peer-ip> source <local-ip>
   ```
2. Check the BGP state of the peer:
   ```sh
   show bgp neighbors <peer-ip>
   ```
   Look for 'BGP state = Active' (cannot connect) or 'Idle (Admin)' (locally shut). Check the 'Hold time' and 'Keepalive interval'.

Next, validate the AS and peer configuration:
- Ensure `neighbor <ip> remote-as <AS>` in your configuration matches the peer's local AS.
- Check for MD5 password mismatches, which can cause BGP notifications like 'BGP notification: hold time expired'. Use:
  ```sh
  debug ip bgp <peer-ip> events
  ```
  Look for NOTIFICATION messages indicating 'cease/hold-timer/open-message-error' subtypes.

For OSPF adjacency issues, it's possible that there is a mismatch in the MTU between the interfaces, especially if one is set to a larger MTU than the other. Ensure that the MTU settings are consistent between the interfaces.

1. Check the MTU settings:
   ```sh
   show interfaces
   ```
2. Verify the MTU settings and align them if necessary. For example, use:
   ```sh
   ip ospf mtu-ignore
   ```
   This command disables the MTU check on the OSPF interface.

For the DHCP pool exhaustion, clear the DHCP bindings to force clients to renew their leases:
1. Run the following command to clear all DHCP bindings:
   ```sh
   clear ip dhcp binding *
   ```
   However, this should only be done in a maintenance window to avoid disrupting service.

Finally, for any DNS resolution issues, ensure that the client is not using the wrong DNS server and that the forwarder chain is correctly set up. Run the following commands to check the DNS configuration:
1. Verify the DNS forwarders:
   ```sh
   show ip name-server
   ```
2. Ensure the forwarder chain is correctly set up


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "First, investigate the BGP session status by running `show ip bgp neighbors <peer-ip>` to check the BGP state. Look for 'BGP state = Active' or 'Idle (Admin)' to determine the issue. Ensure that the `neighbor <ip> remote-as <AS>` configuration matches the peer's local AS. Verify TCP reachability by pinging the peer address from the router VRF using `ping vrf MGMT <peer-ip> source <local-ip>`. Check for ACLs blocking TCP/179 by running `ping vrf MGMT <peer-ip> source <local-ip>`. If TCP/179 is being blocked, the issue lies in the ACLs and needs to be adjusted.

For the OSPF adjacency issues, run `show ip ospf neighbor` to identify stuck states such as INIT, 2-WAY, and EXSTART/EXCHANGE. Check for MTU mismatch by ensuring both interfaces have the same MTU size and use `ip ospf mtu-ignore` on both sides if necessary. Address area-type mismatch by ensuring both routers agree on stub/nssa flags in `area <id> st`. Check MD5 password mismatch by using `debug ip ospf adj` and looking for 'Invalid authentication' messages.

For the DHCP scope exhaustion, immediately mitigate by clearing DHCP bindings with `clear ip dhcp binding *`. Confirm which entries are stale by running `show ip dhcp binding | include Expiry`. For clients on APIPA (169.254.x.x), there are recovery actions such as flushing the DNS cache and adjusting the DHCP lease time. If the issue persists, consider expanding the DHCP scope or adding a new secondary pool.

For DNS resolution failures, isolate DNS vs. network by testing directly to the corp DNS servers. Check for a forwarder chain issue by testing `dig @10.10.1.53 google.com +time=2` if the internal resolution fails. Address zone delegation issues by cross-referencing with the ARP table and ensuring client DNS server assignment is correct. For prevention, consider setting a shorter DHCP lease time or enabling DHCP snooping.

Once each of these components are addressed, revalidate the BGP, OSPF, and DHCP configurations to ensure all settings are correct and the network is stable. If any of the components fail to resolve, escalate the issue.", "cited_artifacts": ["KB


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "The network failures observed after the datacenter fiber maintenance can be attributed to a combination of issues: BGP session failures, OSPF adjacency failures, and DHCP scope exhaustion. Let's address each issue systematically.

For the BGP session failures, ensure that TCP/179 is not blocked by ACLs on either end. Ping the peer address from the router VRF to verify TCP reachability: `ping vrf MGMT <peer-ip> source <local-ip>`. If ping fails, check ACLs for blocking TCP/179.

To check BGP state, use `show bgp neighbors <peer-ip>`. Look for 'BGP state = Active' (can't connect) or 'Idle (Admin)' (locally shut). Check 'Hold time' and 'Keepalive interval'.

For validation, confirm the AS number and peer configuration: `neighbor <ip> remote-as <AS>` should match the peer's local AS. MD5 password mismatch causes 'BGP notification: hold time expired' logs. Ensure that MD5 passwords are correctly configured on both sides.

As for OSPF adjacency failures, inspect `show ip ospf neighbor` to identify stuck states such as INIT, 2-WAY, or EXSTART/EXCHANGE. Common causes include mismatched MTU or area types between peers. Check `show ip ospf` for area flags and ensure that stub/nssa flags are set correctly.

To address DHCP exhaustion alerts, immediately mitigate by reducing lease time from 8 days to 1 day with the command: `ip dhcp pool CORP_WIFI / lease 1`. If the scope is full, consider expanding the scope or adding a new secondary pool. Disable DHCP snooping to prevent rogue DHCP servers consuming addresses.

For the DNS issue, confirm that DNS is the problem before escalating network connectivity issues. Use `nslookup <hostname> <dns-server-ip>` to target the corp DNS. If external resolution fails but internal succeeds, check forwarder connectivity: `dig @10.10.1.53 google.com +time=2`.

Finally, if the issue persists, escalate the ticket and involve the network operations team to conduct a comprehensive network scan and perform physical layer checks on the re-patched cables. Ensure that all interfaces and cables are properly connected and powered.

Please ensure to cross-reference the findings with the relevant KB articles for detailed troubleshooting steps and configuration examples.

Note: The escalation can


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "The GPU-accelerated ML inference service running out of VRAM is likely due to the service's high memory usage and no existing runbook or precedent. The A10G GPU is a powerful but memory-intensive device, and the error `CUDA_ERROR_OUT_OF_MEMORY` suggests that the service is exceeding the GPU's VRAM limit. Here are some steps to address the issue:

1. **Analyze and Optimize the Service:**
   - **Memory Usage:** Use profiling tools like `nvidia-smi` to monitor the GPU's memory usage. Identify which parts of the service are consuming the most VRAM.
   - **Optimize Algorithm and Model:** Check if the model being used can be optimized. Techniques like quantization, pruning, and knowledge distillation can reduce the model size without significantly impacting accuracy.
   - **Data Preprocessing:** Ensure that data preprocessing steps are optimized to minimize memory usage. For instance, reduce image dimensions, use efficient data types, and apply data augmentation techniques judiciously.

2. **Configuration Adjustments:**
   - **TTL Configuration:** For the A10G GPU, consider setting appropriate TTLs for the image classification data in your Redis or any other data storage. Without TTLs, data can fill up the GPU's VRAM indefinitely.
   - **Redis Optimization:** Refer to KB-00019 for best practices. Ensure that Redis is configured correctly to avoid unexpected key expiry and memory pressure. This can prevent the service from running out of VRAM.

3. **JVM Memory Management:**
   - **Heap Dump Analysis:** For JVM OOM errors, use the methods described in KB-00015. Ensure that heap dumps are generated and analyzed to identify any misconfigurations or memory leaks.
   - **Code Review:** Examine the service's codebase for any significant memory leaks. Look for large byte[] or char[] arrays that are not properly managed.

4. **Database Connection Pool Management:**
   - **Monitor and Adjust:** Use Prometheus/Grafana metrics to monitor the connection pool health. Identify and resolve any issues that prevent connections from being released.
   - **Increase Pool Size:** If necessary, increase the connection pool size in the application configuration to handle more concurrent connections.

5. **Scaling and Resource Allocation:**
   - **Resource Allocation:** If the service continues to run out of VRAM, consider scaling up the GPU


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "It seems the GPU-accelerated ML inference service is running out of VRAM due to the limitations of the A10G GPU, which is a new service type with no precedents. The error code (CUDA_ERROR_OUT_OF_MEMORY) suggests there is not enough GPU VRAM allocated for the service to perform the ML inference tasks. Given that this is a new service type, there is no existing runbook or past ticket that directly addresses this issue. However, the recommended steps to manage GPU memory in a new service are as follows:

1. Review the maximum VRAM allocation for the A10G GPU: Check the GPU's specifications to confirm the maximum VRAM allocation. This can typically be found in the hardware documentation.

2. Optimize the model: Ensure the model being used in the inference service is optimized for the GPU. This includes using quantization, pruning, and other model optimization techniques that reduce the model size and improve performance.

3. Adjust model configuration: If the model is too large for the GPU, you may need to adjust the model configuration. This could include downgrading the model or using a smaller version of the model that fits within the available GPU VRAM.

4. Use tensor cores efficiently: The A10G GPU has tensor cores that can be utilized more efficiently for certain types of operations, such as matrix multiplication. Ensure that these cores are being used effectively in your model.

5. Manage memory footprint: For the new service, consider implementing features to manage memory usage. This might include caching results of computations that can be reused, using a cache eviction policy like `allkeys-lru` (as per KB-00019), or setting appropriate TTLs for session keys (as per KB-00015).

6. Monitor and adjust: Continuously monitor the GPU usage and memory footprint of the service. Make adjustments as necessary to ensure that the service is running smoothly within the available VRAM limits.

No specific artifacts or solutions from the retrieved context are directly applicable to managing GPU memory for this new service, so no artifacts need to be cited. The resolution is based on general best practices for managing GPU memory in machine learning inference services.

"cited_artifacts": [], "confidence": 0.85, "escalate": false}}
```


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "It seems that the GPU-accelerated ML inference service on the A10G GPU is encountering a CUDA out-of-memory error (CUDA_ERROR_OUT_OF_MEMORY). This is a new service and there is no existing runbook or past ticket for GPU memory management. For GPU memory issues, ensure the GPU driver is up-to-date and compatible with the CUDA version being used. Additionally, verify the GPU usage in the application through profiling tools to identify whether there are any unused or non-optimized GPU operations. Check the CUDA memory usage by the application with tools like `nvidia-smi` to monitor GPU memory usage. Ensure that the GPU memory allocation and management is optimized, possibly by reducing the batch size or number of images processed per batch if possible. It is also important to check the network traffic since large datasets can put a strain on GPU memory. If the service involves large model inference, consider reducing the model complexity or increasing the GPU memory. Monitor the service's performance and logs to detect any other patterns or potential causes for the out-of-memory errors. Adjust the service configuration accordingly to handle the GPU memory requirements. If the service is using a large number of images or data, consider expanding the GPU memory by using a more powerful GPU or increasing the GPU memory in the existing configuration. If the service is using persistent memory like NVMe, ensure that it is properly configured to handle the GPU memory allocation. If the problem persists, consult the NVIDIA forums or support for additional assistance.", "cited_artifacts": [], "confidence": 0.85, "escalate": false}}
```


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "The GPU-accelerated ML inference service running out of VRAM is likely due to insufficient GPU memory allocation or high memory pressure. Given that this is a new service type without a runbook, the following steps can be taken to diagnose and mitigate the issue:

1. **Memory Pressure Check:**
   - For the inference service, it's crucial to monitor the GPU memory usage. Use tools like nvidia-smi to check the GPU memory utilization. Ensure that the service is configured with an appropriate GPU memory allocation based on the model being run and its parameters.

2. **Increase GPU Memory Allocation:**
   - If the GPU memory usage is consistently high, increase the GPU memory allocation for the service. This can be done by adjusting the batch size, downscaling the model complexity, or using techniques like quantization and pruning to reduce the memory footprint without compromising the model's performance.

3. **Check for Memory Leaks:**
   - Since the GPU memory issues are GPU-specific, focus on ensuring there are no memory leaks in the CUDA/C++ code. Use tools like nvidia-smi to track GPU memory usage over time and look for spikes that could indicate a leak.

4. **Optimize Memory Usage:**
   - Optimize the inference service to minimize unnecessary memory usage. This includes releasing unused memory and ensuring that all allocated buffers are properly managed and deallocated.

5. **Consider Asynchronous Processing:**
   - To handle high GPU memory demands, consider implementing asynchronous processing for inference tasks. This can help manage memory usage more efficiently by offloading some tasks to a separate thread or process.

6. **Monitor and Tune:**
   - Continuously monitor the inference service's performance and memory usage. Use log analysis tools to identify patterns and outliers. Adjust settings like the number of concurrent inferences and memory limits based on the observed performance.

7. **Documentation and Runbook:**
   - Document the process for managing GPU memory in the future. This should include steps for increasing memory allocation, monitoring GPU usage, and troubleshooting common issues. Create a runbook that includes these steps to help manage GPU memory for new and existing services.

In case of persistent issues, escalate to the GPU administrator or hardware support team for hardware-specific optimizations, such as increasing the GPU memory capacity or investigating the hardware for potential failures.

By following these steps, you can diagnose and resolve the GPU memory issues for the new inference service. If the problem persists,


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "The GPU-accelerated ML inference service running out of VRAM is an unexpected scenario since there is no precedent. The issue seems to be related to memory usage and potential misconfigurations within the service or its environment. Ensure that the GPU inference service is using an appropriate amount of VRAM. If the service is not configured to limit its memory usage, it could be consuming more VRAM than expected.

1. Review the service's configuration for any settings that might be causing it to allocate more VRAM than intended. Look into any settings related to GPU memory usage or allocation.

2. Check for any misconfigurations in the GPU driver or the operating system's kernel parameters that might be affecting the memory management.

3. For the GPU itself, ensure it is correctly configured to handle the workload. Incorrect settings can lead to memory leaks or incorrect memory management, causing out of memory errors.

4. Consider using tools like nvidia-smi to monitor GPU memory usage and ensure it is within safe limits. This will help you identify if the issue is with the service itself or the GPU configuration.

5. If the service relies on third-party libraries or frameworks for GPU acceleration, check their documentation and configuration for any memory management settings. Ensure they are optimized for your specific use case.

6. Evaluate if there are any caching mechanisms in place that might be causing excessive memory usage. This could be in the form of session management or other types of caching that are not configured properly.

7. If the service is running on a cluster, check the memory usage of other nodes to ensure they are not contributing to the memory pressure.

8. Consider scaling the service horizontally by adding more GPU nodes if the issue is consistently affecting the same node. This should help distribute the workload and reduce the likelihood of hitting the memory limit.

9. Regularly monitor the service and its environment for any changes that might affect memory usage. Implement automated checks and alerts to quickly identify and address issues before they become critical.

10. Document the findings and any changes made to address the issue. This will help in creating a runbook for future reference and ensure that the issue does not recur.

cited_artifacts": ["KB-00015", "KB-00016"], "confidence": 0.85, "escalate": false}}
```


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "For the GPU-accelerated ML inference service running out of VRAM and hitting CUDA out of memory errors, ensure that the GPU memory allocation is optimized and the inference workload is well-managed. Given the lack of precedent, it's important to start with a basic review and tuning process. First, confirm that the inference workload is configured to utilize GPU memory efficiently. Check the allocation size and buffer size settings in your inference service’s configuration to ensure they are not set too high. Additionally, validate that the inference workload is not holding onto unnecessary data or objects, especially large ones that could contribute to out-of-memory errors.

For GPU memory management, the relevant context from the KB-00015 article on JVM Out of Memory Error — Heap Dump Analysis is not directly applicable since the error is related to VRAM (Virtual Memory) on the GPU rather than the heap memory on the CPU. However, the heap dump analysis techniques and tools mentioned could still be useful in diagnosing any issues related to memory bloat or inefficient use of GPU resources.

Common causes of out-of-VRAM errors on GPUs include:
1. Unbounded cache usage: Ensure that your caching mechanisms, such as using a cache or memcached, are configured correctly. For example, in a Java environment, consider using a cache like Guava or Caffeine with appropriate configuration parameters like `maximumSize`.
2. Holding onto unnecessary objects: Check for any objects that are not properly released or are holding onto large data structures that could cause out-of-VRAM errors.
3. Long-running inference tasks: Ensure that inference tasks are not running for excessively long periods, which can exhaust GPU memory. This might involve optimizing the inference algorithm or the input data.

Immediate recovery steps:
1. Scale up the GPU memory allocation in the inference service’s configuration if necessary.
2. Increase the allocated GPU memory if possible, but be cautious to avoid over-allocation which could cause system instability.

Monitor the GPU memory usage and inference workload to ensure that the service is running efficiently. Consider using profiling tools to monitor memory usage and cache behavior during inference tasks.

If the problem persists after applying these optimizations, escalate to a higher level support or technical team for further investigation. Reach out to the GPU and ML experts within your organization for potential architectural changes or different hardware configurations that might better suit the workload requirements.",
"cited_artifacts": [], "confidence": 0.85, "escalate":


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "The GPU-accelerated ML inference service running out of VRAM can be addressed by increasing the GPU memory or optimizing the service to use less VRAM. To start, ensure that the GPU inference service is correctly configured to utilize the A10G GPU. Check the CUDA configuration to ensure that the service is not set to use more VRAM than the GPU has available. If scaling the GPU memory is not an option, consider optimizing the service by reducing the batch size or using quantization techniques that reduce the amount of data processed on the GPU. Additionally, check if there are any memory leaks within the service, which could be indicated by an increasing used_memory_rss value in Redis, as described in KB-00019. For database connection pool exhaustion, ensure that the connection pool settings are appropriate and monitor for long-running queries or unclosed ResultSets, as detailed in KB-00016. Implement auto heap dump on OOM with JVM flags as described in KB-00015 to analyze heap dumps for root cause. Address any misconfigurations such as unbounded caches (e.g., Guava Cache or Caffeine without `maximumSize` configured) and session object bloat. To handle out-of-memory errors in the inference service, also consider increasing the available GPU memory or using tools like cuDNN to optimize tensor operations.", "cited_artifacts": ["KB-00015", "KB-00001", "KB-00019", "KB-00016"], "confidence": 0.85, "escalate": false}}
```


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "For the GPU-accelerated ML inference service running out of VRAM on the A10G GPU, the issue appears to stem from insufficient memory management due to the use of a GPU that is not optimized for high memory usage, especially in the context of GPU memory fragmentation and resource allocation. Ensure that the GPU driver and CUDA toolkit are up-to-date, and configure the memory usage settings correctly. Check the memory usage through the GPU's control panel to see if there are any settings that can be adjusted to allow more VRAM to be used. If the issue persists, consider using a different GPU model that is more suitable for high VRAM usage in machine learning tasks. Additionally, ensure that the inference service itself is configured to manage memory properly, such as by using appropriate batch sizes and model configurations to reduce the amount of VRAM required per inference run. For instance, increasing the batch size can reduce the number of times the model needs to be loaded, thereby reducing VRAM usage. If the service is memory-intensive, it might be beneficial to offload some of the processing to the CPU to reduce GPU memory pressure. Review the TensorFlow or PyTorch documentation for best practices on managing GPU memory in ML inference services.", "cited_artifacts": [], "confidence": 0.7, "escalate": false}}
```


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "The error 'Connection is not available, request timed out after 30000ms' in the inventory-service is indicative of connection pool exhaustion in the application. Given that the database server is healthy, the issue likely lies within the application's connection handling, specifically the connection pool configuration. First, confirm if the connection pool is saturated by checking Prometheus/Grafana metrics for `hikaricp_connections_active` and `hikaricp_connections_max`. If the pool is indeed saturated, this suggests that the pool is being exhausted and not being managed efficiently. The current number of connections should ideally be less than or equal to `hikaricp_connections_max` to prevent timeouts. To resolve this, you should increase the `hikaricp_connections_max` setting to allow for more connections in the pool. Additionally, monitor the number of active connections (`hikaricp_connections_active`) and `hikaricp_connections_pending` to ensure that the pool is not holding too many idle connections. If the issue persists, review the thread dumps to identify any threads that are consuming excessive resources or remaining in a long-running state, which could indicate a resource leak. Ensure that the application is configured correctly to handle the number of connections it is managing. Clearing the connection pool by restarting the application service can also help release idle connections and free up the pool. If the problem continues, review the application logs for any exceptions or errors that might be causing connections to be left open longer than they should. Finally, ensure that the application's thread pools are properly configured and that there are enough threads available for new work requests.", "cited_artifacts": ["KB-00016"], "confidence": 0.85, "escalate": false}}
```


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "The error messages indicate that the inventory-service is experiencing connection pool exhaustion, causing it to time out after 30000ms. First, let's confirm if the connection pool is saturated by checking the Prometheus/Grafana metric 'hikaricp_connections_active'. If it equals 'hikaricp_connections_max', the pool is indeed saturated. You can also check for high 'idle' connections in the database using the `SHOW PROCESSLIST;` command (for MySQL) or `SELECT count(*), state FROM pg_stat_activity GROUP BY state;` (for PostgreSQL). This will help identify idle connections that are not being used but are still holding onto database resources.

Next, find the connection holders by examining the application's thread dumps using `kill -3 <pid>` in the container. Look for threads in a `RUNNABLE` or `WAITING` state with JDBC/database stack frames. Long-running queries or unclosed ResultSets are common culprits for this issue.

Once we've identified the problematic connections, ensure that the application is properly closing these connections. This involves checking for any unclosed connections or result sets that could be holding onto database resources.

Additionally, review the application code for potential memory leaks, especially in the connection handling and pooling logic. Tools like JProfiler can help identify and debug memory usage patterns within the application.

To mitigate the issue, consider increasing the connection pool size, especially during peak hours, or fine-tune the connection pool settings to better match application demands. Ensure that there are no unnecessary threads or processes holding onto database resources.

To prevent future occurrences, implement a robust connection pooling mechanism that includes connection validation and idle connection eviction. This can help reclaim resources and prevent the pool from becoming saturated.

It is also recommended to regularly monitor the database connection metrics and application logs to proactively address any issues before they affect service availability.

After implementing these changes, monitor the application logs and database statistics to ensure that the connection pool exhaustion is resolved. Restart the application service if needed to flush the pool.

If the issue persists, investigate further by looking at the application crash logs and events using `kubectl logs <pod> --previous` and `kubectl describe pod <pod>`, and check for any root causes such as configuration errors or memory issues.

For further assistance, consider consulting the application's codebase and documentation to ensure that the connection handling and pooling logic are optimized for the expected workload.", "cited


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "The connection pool timeout errors in the inventory-service can be traced back to the connection pool exhaustion. First, ensure the maximum pool size (`hikaricp_connections_max`) is adequate to handle the load. Examine the active connections (`hikaricp_connections_active`) to identify if connections are being held by long-running queries. Check the database side for idle connections (`SELECT count(*), state FROM pg_stat_activity GROUP BY state;` for PostgreSQL or `SHOW PROCESSLIST;` for MySQL), which might indicate a connection pool issue.

To diagnose the issue, retrieve the thread dump from the application to find threads in `RUNNABLE` or `WAITING` state with JDBC/database stack frames, which are indicative of long-running queries or unclosed ResultSets. Review the slow queries using the `EXPLAIN` command and consider adding indexes if necessary.

For the database side, ensure there are no unclosed connections by checking the state of `pg_stat_activity` or `SHOW PROCESSLIST` and close any idle connections. Monitor the connection pool metrics using Prometheus/Grafana and adjust the connection pool size as needed.

Additionally, verify the upstream service's health. Use the API gateway access logs to identify slow upstream services by filtering for 504 errors. Test the upstream directly bypassing the API gateway to confirm the issue is not with the upstream service. If the issue persists, increase the `proxy_read_timeout` in Kong to 60000 seconds to accommodate the increased latency.

To resolve the CrashLoopBackOff issue in Kubernetes, get the last crash logs with `kubectl logs <pod> --previous` and check the events with `kubectl describe pod <pod>` to identify the root cause. Common issues include memory limits being too low, missing secrets, misconfiguration, or a liveness probe that is too aggressive.

Restart the application service to flush the connection pool and ensure the application is healthy. If Kubernetes restarts the pod, check the pod events and logs to troubleshoot the root cause of the CrashLoopBackOff.

Ensure all configurations, timeouts, and memory limits are appropriately set to avoid connection pool exhaustion and CrashLoopBackOff issues.

Ensure to monitor the system and make adjustments as necessary to maintain optimal performance and reliability.", "cited_artifacts": ["KB-00016", "KB-00017", "KB-00001", "KB-00


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "The 'Connection is not available, request timed out after 30000ms' errors in inventory-service during peak hours (18:00-19:00) could be due to connection pool exhaustion on the database side. Check the Prometheus/Grafana metric `hikaricp_connections_active` to confirm the pool is saturated. Additionally, check `hikaricp_connections_pending` to see if there are any connections waiting in the pool. Look for long-running queries or unclosed ResultSets in the application thread dumps, which can cause the pool to exhaust. On the database side, review the `pg_stat_activity` or `SHOW PROCESSLIST` query to see if there are idle connections not being used and queries running for an extended period. To recover, restart the application service to flush the connection pool. Ensure that the `hikaricp_connections_max` setting is adequate for the expected load, and consider increasing the `hikaricp_ping_interval` if necessary to reduce ping overhead. Investigate if the cause is external to the service, such as network latency affecting the connection pool. If the issue persists, consider adding a new connection pool to distribute load or increasing the maximum number of connections to prevent saturation. Adjust the connection timeout settings in the database configuration if applicable. Finally, monitor the connection pool usage and make necessary adjustments based on the observed behavior.", "cited_artifacts": ["KB-00016"], "confidence": 0.85, "escalate": false}}
```


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "The connection pool timeout errors in the inventory-service can likely be attributed to a lack of connections available due to exhaustion, as indicated by the connection pool metrics being saturated. First, confirm that the connection pool exhaustion is occurring by checking the Prometheus/Grafana metric `hikaricp_connections_active` against `hikaricp_connections_max`. This indicates that the pool is at its maximum capacity and cannot accommodate new requests. In cases of connection pool exhaustion, you should monitor the `hikaricp_connections_pending` metric to identify which connections are stuck and not being used.

To address this issue, you should immediately attempt to flush the connection pool by restarting the application service. This will clear any stale connections and allow the pool to release them back into the available pool. Monitor the connection pool metrics after restarting the service to ensure they are normalized, and check the application logs for any clues about the cause of the exhaustion, such as frequent long-running queries or unclosed ResultSets.

Also, it's important to look at the application threads for any long-running tasks or queries. Using a thread dump command like `kill -3 <pid>` can help identify threads in a RUNNABLE or WAITING state, which might indicate a problem in long-running queries or unclosed database connections. Check for any suspicious database activity or queries in the thread dump that are causing the application to hang.

Additionally, ensure that the application is not holding onto too many database connections. Review the database connections to see if there are any idle connections that are not being properly released. In PostgreSQL, you can use `SELECT count(*), state FROM pg_stat_activity GROUP BY state;` to see the counts of idle and active connections.

For the API Gateway, which is causing the 504 Gateway Timeout errors, it's important to investigate the upstream service directly, as the gateway's logs (access.log) might not provide the full picture. Use `curl -w '%{time_total}' -o /dev/null http://<upstream-host>:<port>/endpoint` to test the upstream service directly. If the timeout occurs on the upstream service, look for regression in database queries, dependency slowness, or thread pool exhaustion at the upstream level.

Lastly, check the Kubernetes pod events for any errors or warnings that may provide additional insights into why the pod is crashing. Look for messages like 'OOMKilled', 'Error' (in the container logs),


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "The connection pool timeout errors in inventory-service are likely due to database connection exhaustion. Since the database server is healthy, the issue is likely within the application's connection pool. To resolve this, follow these steps:

1. **Monitor the Connection Pool**: Use Prometheus and Grafana to monitor the `hikaricp_connections_active` and `hikaricp_connections_pending` metrics. If these metrics are both high, it indicates the pool is saturated.

2. **Identify Long-Running Queries**: Examine thread dumps for any threads in the `RUNNABLE` or `WAITING` state with JDBC/database stack frames. This can help identify long-running queries that are holding onto connections. Additionally, check `SHOW PROCESSLIST;` in PostgreSQL or `psql -c "SELECT count(*), state FROM pg_stat_activity GROUP BY state;"` in PostgreSQL to find idle connections that are not being freed.

3. **Restart the Application Service**: To flush the connection pool, restart the inventory-service application service. Monitor the `hikaricp_connections_active` metric post-restart to ensure the pool is being cleared.

4. **Investigate Slow Queries**: Use the explain plan to identify slow queries. Optimize queries by adding indexes, rewriting them, or improving their performance.

5. **Adjust JDBC Settings**: Increase the `maxPoolSize` of the HikariCP configuration if the pool is often reaching its maximum capacity. Ensure the `connectionTimeout` and `idleTimeout` settings are set appropriately.

6. **Check Database Configuration**: Verify the database configuration such as query timeout, connection settings, and maintenance activities.

By following these steps, you should be able to resolve the connection pool timeout errors and ensure the service can handle load effectively. If the issue persists, consider investigating the upstream service causing the 504 Gateway Timeout errors as mentioned in KB-00018.

Cited Artifacts: [KB-00016, KB-00018]

Confidence: 0.85

Escalate: false", "cited_artifacts": ["KB-00016", "KB-00018"], "confidence": 0.85, "escalate": false}}
```


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "The connection pool timeout errors in inventory-service could be related to the database connection pool exhaustion rather than an API gateway timeout issue. First, confirm that the connection pool is indeed exhausted by checking the Prometheus/Grafana metrics for `hikaricp_connections_active` and `hikaricp_connections_max`. If `hikaricp_connections_active` equals `hikaricp_connections_max`, it indicates the pool is saturated. Next, find the connection holders by examining the application thread dump with `kill -3 <pid>` and searching for threads in `RUNNABLE` or `WAITING` states with JDBC/database stack frames. This could indicate long-running queries or unclosed ResultSets that are holding onto connections. On the database side, use commands like `SELECT count(*), state FROM pg_stat_activity GROUP BY state;` for PostgreSQL or `SHOW PROCESSLIST;` for MySQL to check the status of connections. High 'idle' connection counts indicate the pool holding connections not in use, while high 'active' counts point to queries running long. To immediately recover, restart the application service to flush the connection pool. Monitor that connections are being released properly. If the issue persists, check the Kubernetes logs for the inventory-service pod to see if there are any specific reasons for the CrashLoopBackOff. Look for common root causes like OOMKilled (memory limit hit), missing secrets, config errors, or overly aggressive liveness probes. If the root cause is determined to be a misconfiguration or an external dependency slow, adjust the respective configurations accordingly.", "cited_artifacts": ["KB-00016", "KB-00017"], "confidence": 0.85, "escalate": false}}
```


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "The 'Connection is not available, request timed out after 30000ms' errors in inventory-service during peak hour could be due to connection pool exhaustion. Check the application logs for a high 'idle' connection count in the Prometheus/Grafana metric `hikaricp_connections_idle`. This indicates the connection pool may be holding idle connections that are not being released and are waiting for a new request, leading to timeout errors. To resolve this, the application service should be restarted to flush the connection pool. Monitor that the `hikaricp_connections_idle` metric decreases and the `hikaricp_connections_pending` metric returns to zero. Additionally, review the database side for any queries running long ('active' state in `pg_stat_activity` for PostgreSQL or `SHOW PROCESSLIST;` for MySQL) that are holding onto connections. Address any long-running queries and optimize the database query plan if necessary. Ensure the application and database configurations are correctly aligned to prevent connection pool exhaustion. Restart the application service to flush the connection pool and monitor the issue.", "cited_artifacts": ["KB-00016"], "confidence": 0.85, "escalate": false}}
```


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "The issue with the site-to-site VPN tunnel to the Singapore branch office dropping every 20-40 minutes can likely be attributed to the Phase 1 (IKEv2) parameter mismatch, causing the IKE protocol to fail and the tunnel to flap. To troubleshoot, follow these steps:

1. Capture the NOTIFY payload from the Cisco IPSec IKEv2 debug logs. Ensure both ends are using identical IKE proposals, including encryption (AES-256), PRF (SHA-256), DH group (14 or 19), and lifetime (86400 seconds). Even the slightest mismatch in these parameters can cause the tunnel to immediately drop.

2. Check the Dead Peer Detection (DPD) configuration. If the WAN latency spikes above 20 seconds, the DPD timers might be too aggressive, causing the tunnel to drop. Adjust the DPD timers on both ends by setting them to `dpd 60 retry 5` to better handle high latency.

3. Ensure both ends support NAT-T (Network Address Translation Traversal) by adding the following commands on both routers:
   ```
   ip ipsec ike nat-traversal
   ip ipsec ike peer nat-traversal
   ```
   Verify that the NAT-T flag is present in the IKE SA (Security Association) show command on both sides.

4. Examine the `debug ip bgp <peer-ip>` output for BGP state transitions. Look for the 'BGP state = Active' or 'Idle (Admin)' status. Ensure that the `neighbor <ip> remote-as <AS>` command on both ends matches the remote AS number. Any configuration mismatch can cause BGP sessions to fail.

5. Check for MTU mismatch, which can prevent the BGP adjacency from forming. Ensure that the MTU values on both interfaces are equal.

6. Review the logs to identify any 'NOTIFICATION' messages that indicate subtypes like 'CEASE' or 'HOPLIMIT'. These logs can provide information on the cause of the session drop.

7. For TCP connectivity issues, use `ping vrf MGMT <peer-ip> source <local-ip>` to verify that TCP/179 is not blocked by ACLs on the local router.

By aligning these parameters and ensuring proper configuration, the tunnel should stabilize and no longer flap. Additionally, monitor the API Gateway's 504


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "The issue with the site-to-site VPN tunnel to your Singapore branch office is likely due to a combination of causes related to both the BGP peer session and the IKEv2 phase. Let's troubleshoot and resolve these two aspects separately.

**BGP Troubleshooting:**
1. **Verify TCP reachability:** Ensure that TCP/179 is allowed through the appropriate ACLs on both the branch office and your main site routers. Verify reachability using the command `ping vrf MGMT <peer-ip> source <local-ip>`.
2. **Check BGP state:** Use the command `show bgp neighbors <peer-ip>` to identify the BGP state of the peer. It should be in 'Established' state if the connection is up. Look for any 'BGP Notification' messages indicating a hold-time expiration, which may point to hold-timer mismatch or configuration mismatch.
3. **Validate AS and peer configuration:** Confirm that `neighbor <ip> remote-as <AS>` matches the peer's local AS. Check for MD5 mismatch between the encryption parameters of both ends.
4. **Review logs:** Enable debugging for IP BGP events using `debug ip bgp <peer-ip> events` on both sides. Look for 'NOTIFICATION' messages with 'BGP notification: hold time expired' or 'NOTIFICATION' messages indicating other types of errors.

**IKEv2 Troubleshooting:**
1. **Capture NOTIFY payload:** Use `debug crypto ikev2` on both ends to capture the NOTIFY payload. Look for `NO_PROPOSAL_CHOSEN` in the error logs indicating mismatch in IKE proposals, such as algorithm (e.g., AES-256, SHA-256), PRF (e.g., SHA-256), Diffie-Hellman group (e.g., 14 or 19), and lifetime (e.g., 86400s).
2. **Algorithm mismatch:** Ensure that both peers use identical IKE proposal details.
3. **Dead Peer Detection (DPD):** Check `dpd` timers on both ends. Default DPD timers should be `dpd 30 retry 5`. If WAN latency spikes over 20 seconds, increase the timers to `dpd 60 retry 5`.
4. **NAT-T configuration:** If one end is behind NAT, NAT-T must


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "The site-to-site VPN to your Singapore branch office is dropping due to a combination of factors. First, investigate the VPN tunnel flapping by capturing the NOTIFY payload using `debug crypto ikev2` on both the client and the remote gateway. This will help identify the cause of the phase 1 or phase 2 parameter mismatch. Look for 'NO_PROPOSAL_CHOSEN' in the logs, which indicates an algorithm mismatch. Ensure both ends use identical IKE proposals: encryption (AES-256), PRF (SHA-256), DH group (14 or 19), and lifetime (86400s). Aligning these will prevent the VPN tunnel from dropping immediately.

For the BGP peer session drops, first ensure TCP connectivity by pinging the peer address from the router VRF with the `ping vrf MGMT <peer-ip> source <local-ip>` command. If pinging fails, check for ACLs blocking TCP/179 on both ends.

Next, verify the BGP state. Use `show bgp neighbors <peer-ip>` to look for 'BGP state = Active' or 'Idle (Admin)', indicating a dropped connection. Check 'Hold time' and 'Keepalive interval' to ensure they are aligned. The AS and peer configurations must match between both ends. Validate that the MD5 password is correct, as a mismatch will cause a 'BGP notification: hold time expired' log.

Review the BGP logs for any 'BGP notification' messages indicating a 'NOTIFICATION' subtype. After identifying the cause, apply the necessary configuration changes. Ensure that DPD timers are properly configured: use `neighbor <ip> timers <keepalive> <hold>` to adjust the timers or clear the ACL blocking TCP/179.

Finally, address any potential issues with the BGP AS number or MD5 password mismatch by correcting them in the BGP configuration. Once resolved, the BGP session should stabilize and prevent the connection drops.

Additionally, since the WAN latency to the Singapore branch averages 180ms, the IKE aggressive DPD timers should be set to 60 seconds with a retry of 5 seconds, which aligns with the typical latency window. Set this with the command `neighbor <ip> timers 60 5`.

Lastly, ensure that the MTU of both interfaces is the same and align with the full MT


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "The issue with the Site-to-Site VPN to the Singapore branch office dropping and reestablishing every 20-40 minutes can likely be traced to several potential causes. Let's focus on common ones. First, ensure the MTU between both sides of the router matches. Use `show ip ospf neighbor` to identify stuck states, and check if they are due to different subnet masks or different MTUs. If MTU differences are found, resolve them by either aligning the physical MTU or setting `ip ospf mtu-ignore` on both interfaces. Secondly, check the BGP state between the routers. Run `show bgp neighbors 198.51.100.22` to see if there are any BGP state issues. Any 'BGP state = Active' might indicate a connection issue. Ensure the 'Hold time' and 'Keepalive interval' match between both routers. Also, validate that the `neighbor <ip> remote-as <AS>` on both routers match the peer's local AS. MD5 password mismatches are also known to cause 'BGP notification: hold time expired' logs. Check logs for any such messages. For NAT-T, if one end is behind NAT, ensure NAT-T is enabled on both sides. Check `show crypto ikev2 sa` for NAT-T flags. For IPSec IKEv2, verify that both ends use identical IKE proposal: encryption (AES-256), PRF (SHA-256), DH group (14 or 19), and lifetime (86400s). Even one mismatch causes immediate teardown. Lastly, check DPD (Dead Peer Detection) timers. If the WAN latency spikes above 20 seconds, increase the DPD timers to `dpd 60 retry 5`. If there are no NAT-T and IKE mismatches, and DPD settings are correct, the issue might be due to high latency causing the tunnel to drop. In such cases, consider checking the WAN link conditions and potentially improving the network infrastructure between the headquarters and Singapore. Ensure that the site-to-site tunnel is not congested or experiencing high packet loss. Analyze the access logs for the API gateway to check for timeout issues, but since this is not relevant to the VPN issue, address the identified BGP and IPsec issues first. Once these are fixed, reestablish the connection


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "The issue with the Site-to-site VPN tunnel dropping and reestablishing every 20-40 minutes is likely due to a misconfigured BGP hold timer mismatch between the branch office and the main office. Check the BGP hold timers on both routers and ensure they match. You can verify this by running `show ip bgp neighbors` on both ends and checking the 'Hold time' and 'Keepalive interval.' For example, you might see a message like 'BGP neighbor is up and running, hold time is 180s,' indicating the hold timer is set correctly. Additionally, ensure that the authentication settings (MD5 keys) match on both routers to prevent silent BGP sessions drops. You can check the MD5 keys with `show ip bgp neighbors <peer-ip>` and look for 'bgp peer remote-as' and 'BGP notification.' If there are any mismatches, correct them by setting the MD5 password on both routers using `neighbor <ip> password <new-md5-key>` and then clearing the BGP adjacency with `clear ip bgp <peer-ip> neighbors`. Also, consider adjusting the BGP timers if necessary, but be cautious as overly aggressive timers can lead to frequent drops. For example, if the branch office has higher latency, you might need to increase the hold timers on the branch router to accommodate the longer responses, like `neighbor <ip> timers <keepalive> <hold>` to `neighbor <ip> timers 60 180` to increase the interval between BGP keepalives and hold timers, respectively. If the problem persists, consider checking for MTU mismatches on the interfaces connected to the branch office, which can be done by running `show ip ospf neighbor` to ensure both routers have matching MTU values and setting `ip ospf mtu-ignore` on the interfaces if needed. Lastly, ensure that there are no ACLs blocking TCP/179 between the routers, as this can prevent BGP sessions from coming up, as per KB-00002. Once BGP settings are corrected, the VPN tunnel should stabilize and experience fewer interruptions.", "cited_artifacts": [], "confidence": 0.85, "escalate": false}}
```


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "The issue with the Site-to-site VPN tunnel dropping and reestablishing on its own every 20-40 minutes to the Singapore branch office could be due to a mismatch in the Phase 1 and Phase 2 parameters of the IKEv2 protocol. Specifically, the negotiation of encryption algorithms, PRF, and DH group versions must be identical on both ends to avoid 'NO_PROPOSAL_CHOSEN' notifications. Additionally, ensure that the Dead Peer Detection (DPD) timers are configured correctly. Check both sides for the following parameters and align them:

For IKEv2 Phase 1:
- Encryption algorithms: Ensure both sides use AES-256 for both encryption and PRF (SHA-256).
- DH group: Ensure both sides use the same DH group (e.g., 14 or 19).
- Lifetime: Both sides should have the same IKE proposal lifetime (86400s).

For IKEv2 Phase 2:
- Encryption algorithms: Ensure both sides use AES-256 for both encryption and PRF (SHA-256).
- Lifetime: Ensure the proposal lifetime for Phase 2 matches the Phase 1.

For DPD:
- Aggressive DPD timer: Use `dpd 30 retry 5`. Increase the timers if WAN latency spikes more than 20 seconds.

After configuring these parameters, verify the tunnel health by checking the tunnel status and neighbor statistics. Use `debug crypto ikev2` to capture and analyze IKEv2 messages for any errors, and ensure the tunnel reestablishes without dropping again. If the issue persists, check the NAT-T configuration on both sides to confirm it is enabled (UDP 4500). If NAT-T is not enabled on one side, the tunnel may not be able to establish a phase 1 connection.

Regarding the 180ms WAN latency, the high latency could cause packets to be lost, which can lead to phase 2 retransmissions. Ensure that the retransmission timer on the Phase 2 is set appropriately to handle the higher latency. If the WAN latency is high, consider increasing the retransmission timer to handle the network conditions. Adjust the retransmission timers using the following commands on Cisco IOS:
- For Phase 2 retransmit timer: `crypto isakmp policy 1 retransmit-initial 3 retransmit-period


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "The issue with the site-to-site VPN tunnel dropping every 20-40 minutes and reestablishing on its own appears to be related to incorrect IKE proposal parameters in the IPSec phase 1, causing the tunnel to flap. Here's how to address this:

1. Verify the IKE proposal parameters on both ends. The most common reason for flap is mismatch in the encryption algorithm (AES-256), PRF (SHA-256), DH group (14 or 19), and lifetime (86400s). Use the debug command to capture the NOTIFY payload and check for 'NO_PROPOSAL_CHOSEN' messages to identify the mismatch. Ensure both ends use identical IKE proposals.

2. For NAT-T if one end is behind NAT, enable NAT-T on both ends by setting `crypto ikev2 nat-traversal` and `crypto ikev2 ipsec-proposal xxx` in your configuration.

3. To correct the IKE proposal mismatch, modify the proposal and restart the IKE process:
   On the tunnel interface:
   ```
   crypto ikev2 ipsec-proposal P0
   encryption AES-256
   integrity SHA-256
   group 14
   lifetime 86400

   crypto ikev2 ipsec-proposal P1
   encryption AES-256
   integrity SHA-256
   group 14
   lifetime 86400

   crypto ikev2 ipsec-proposal P2
   encryption AES-256
   integrity SHA-256
   group 14
   lifetime 86400

   crypto ikev2 ipsec-proposal P3
   encryption AES-256
   integrity SHA-256
   group 14
   lifetime 86400

   crypto ikev2 ipsec-proposal P4
   encryption AES-256
   integrity SHA-256
   group 14
   lifetime 86400

   crypto ikev2 ipsec-proposal P5
   encryption AES-256
   integrity SHA-256
   group 14
   lifetime 86400
   ```

   Then restart the IKE process


Response:
```json
{"tool_name": "submit_resolution", "arguments": {"resolution": "The site-to-site VPN tunnel between the headquarters and the Singapore branch keeps dropping due to issues with the Phase 1 (IKE) configuration, specifically with the IKE proposal mismatch. Both ends should be configured to use the same encryption (AES-256), PRF (SHA-256), DH group (19), and lifetime (86400s) in the IKE proposal. Misconfigured MD5 passwords can also cause the tunnel to drop. Additionally, verify that the MTU settings on both ends of the tunnel are consistent, as mismatched MTUs can lead to EXSTART/EXCHANGE stuck states in OSPF, although this is less likely in a site-to-site VPN context.

For the ping test, it should be performed from the router VRF, not directly from the user's workstation. Ensure the subnet mask and hello/dead intervals match on both ends. If the tunnel is intermittently dropping, it's also important to check the DPD timers, as aggressive DPD may cause the tunnel to drop under high latency. Ensure that the dead-peer-detection timers are set correctly on both sides, or consider increasing them if WAN latency spikes. Verify that NAT-T is enabled if one end is behind NAT, and ensure it's present on both sides with UDP 4500.

To ensure TCP/179 reachability, run `ping vrf MGMT <peer-ip> source <local-ip>` from the router to confirm the connectivity. Check the BGP state on the router for the peer: `show bgp neighbors <peer-ip>` to look for 'BGP state = Active'. Validate the remote AS number in `neighbor <peer-ip> remote-as <remote-as-number>` matches the peer's local AS. If you see 'BGP notification: hold time expired', it means the peer's hold timer has expired due to configuration issues, which can be addressed by adjusting the hold-timer values with `neighbor <peer-ip> timers <keepalive> <hold>`.

For the WAN latency to Singapore of 180ms, ensure the DPD timers are set appropriately to handle this latency without causing tunnel drops. The default DPD settings should be sufficient unless latency spikes significantly. Adjust DPD timers if necessary. Finally, test the upstream service for the 504 timeout issue, as described in KB-00018, and make any necessary adjustments to the