DevOps/SRE Copilot

The Problem

Modern infrastructure generates an enormous volume of signals — logs, metrics, traces, alerts. The challenge isn't collecting data, it's making sense of it fast enough to matter. Alert fatigue is real. Incidents that span multiple services take too long to diagnose. Postmortems get written once and forgotten.

Your team's operational knowledge lives in runbooks (often outdated), Slack threads (impossible to search), and people's heads (not available at 3am).

What the Agent Does

• Multi-source monitoring: ingest signals from Datadog, PagerDuty, Sentry, CloudWatch, and your logging stack
• Incident correlation: connect alerts across services to identify root causes, not just symptoms
• Incident summaries: draft real-time summaries and postmortems as incidents unfold
• Runbook execution: suggest runbook steps and auto-execute safe remediations (restart a service, scale up a pool, toggle a feature flag)
• Deployment health: track the impact of every deploy — "last release increased error rate 3x on the checkout service"

What Makes This Different

Generic AI assistants can read logs. This agent knows your architecture — which services depend on which, who owns them, what failed last time, and what fixed it. It has context.

When the payments service starts throwing 500s, the agent doesn't just say "payments is down." It says: "Payments error rate spiked 5 minutes after the auth-service deploy. Similar pattern to incident #247 last month. Rollback resolved it then. Auth-service owner is @sarah."

Getting Started

Connect your observability stack, feed the agent your service catalog and ownership map, and point it at your incident history. It starts correlating from day one and gets sharper as it sees more incidents.