> ## Documentation Index > Fetch the complete documentation index at: https://adapt.com/docs/llms.txt > Use this file to discover all available pages before exploring further. # AI engineering and DevOps workflows > Learn how engineering and DevOps teams use Adapt to manage infrastructure, respond to incidents and automate technical workflows across business tools. Adapt helps engineering and DevOps teams monitor infrastructure, debug production issues, and automate incident response across GCP, GitHub, and Linear. ## Key Use Cases ### Production Error Investigation Automatically investigate production errors and get root cause analysis. ``` Check the GCP logs for errors in the last hour ``` ``` Debug why the production pod is in CrashLoopBackOff ``` ``` Trace this error through our logs and codebase ``` ### GKE & Kubernetes Management Manage and debug Kubernetes clusters. ``` What's the status of our GKE deployments? ``` ``` Show me pods that have restarted in the last 24 hours ``` ``` Check if Cloud NAT is configured correctly in our VPC ``` ### Automated Incident Response Set up webhooks to trigger Adapt when production issues occur. Adapt can: * Investigate logs and find root causes * Correlate errors with recent deployments * Open Linear issues with detailed findings * Create GitHub PRs to fix issues ``` When you see a production error, investigate it, create a Linear issue, and open a PR to fix it ``` ### Infrastructure Setup Use Adapt to run infrastructure commands and configure GCP resources. ``` Create a Cloud NAT router for our staging environment ``` ``` Set up a static external IP for our NAT gateway ``` ``` Check what permissions our service account has ``` ### Log Analysis Query and analyze logs across your infrastructure. ``` Summarize the warning and error logs from the past 24 hours ``` ``` Which errors are expected based on our code vs actual bugs? ``` ``` What's causing the Slack missing scope warnings? ``` ### Daily Operations Automate routine checks and reporting. ``` Every morning at 9am, check our warning and error logs and post a summary to #production-errors ``` ``` Alert me if any pods enter CrashLoopBackOff ``` ``` Track deployment frequency and success rate ``` ## Integrations Used | Integration | Secret Name | Purpose | | ----------- | -------------------------- | ----------------------------- | | GCP | `GCP_SERVICE_ACCOUNT_JSON` | Cloud Logging, GKE, BigQuery | | GitHub | OAuth (native) | Code search, PRs, deployments | | Linear | `LINEAR_API_KEY` | Issue creation and tracking | | Sentry | `SENTRY_ACCESS_TOKEN` | Error monitoring correlation | ## Example Workflow: End-to-End Incident Response 1. **Detect**: GCP Alert fires for high error rate 2. **Investigate**: Adapt queries Cloud Logging for error details 3. **Correlate**: Checks recent GitHub deployments and Sentry errors 4. **Document**: Creates Linear issue with root cause analysis 5. **Fix**: Opens GitHub PR with suggested fix 6. **Notify**: Posts summary to Slack ## Setup 1. Add `GCP_SERVICE_ACCOUNT_JSON` secret with appropriate permissions 2. Connect GitHub via OAuth 3. Add `LINEAR_API_KEY` for issue tracking 4. Create a GCP webhook for alert-triggered investigations Start with read-only permissions for GCP, then expand as you identify specific automation needs.