Production incidents expose what a coding agent cannot learn from the repository alone. Today we're bringing Steadwing into the agent workflow and closer to the evidence from production with our new Agent Skill and Python + Node SDKs.

Most production incidents trace back to something that changed recently. A deploy. A config update. A merged PR. Steadwing connects to your GitHub or GitLab repos and automatically correlates incidents with recent commits, deployments and releases. When something breaks, the RCA shows you which specific change caused it down to the exact commit and code diff. No more asking "did anyone deploy something?" in Slack. No more manually scanning CI/CD pipelines. The correlation happens automatically as part of the investigation.

The AI for incident response space went through two very different eras. AIOps was about anomaly detection. Structured metrics. The output was a correlation score and an alert. No diagnosis. No code understanding. Remediation was mostly "roll back". LLM-powered RCA is a completely different approach. It reads logs, metrics, traces, code diffs, configs, and deploy history. It generates a natural language root cause analysis with an evidence chain you can actually verify. It references past incidents. It suggests fixes on code level, infra and CI/CD. The shift isn't just better models. It's a fundamentally different architecture from pattern-matching on metrics to actually understanding what your system is doing and why it broke. That's the approach we took with Steadwing to show evidence-based RCA with fixes you can verify and immediately ship.

30 alerts in 2 minutes. Your on-call engineer opens Slack and gets overwhelmed. It's rarely 30 problems, usually one issue causing a chain reaction. They end up triaging each alert & 20 minutes are gone. We group alerts into 1 incident & give root cause for it.

We’re now SOC 2 Type II compliant!