Owain Lewis walks through an autonomous "AI SRE" agent he built with the Claude Agents SDK: production alerts flow into a queue, the agent investigates logs, metrics and deployment history, writes its findings to a database, and surfaces them in a custom web console where an operator can approve actions like a rollback. He demos it detecting a deliberately broken deploy (payment config error causing 500s), recommending and executing a rollback, then a second case of a slow-but-working deployment diagnosed via chat. His main caveat: these agents are superhuman at digging through data but routinely over-index on single signals and jump to conclusions, so critical actions should stay human-in-the-loop.
- Downtime is the business case: he cites Amazon losing roughly $72.8 million per hour, and figures in the range of tens of thousands of dollars per minute for large companies, with reputation damage on top of lost revenue.
- The agent follows an OODA-style loop — observe alerts, orient by gathering logs/metrics/deploy history, decide on a root cause, then act by either fixing something or escalating to a human.
- Architecture: alert fires in production, goes into a queue, a Claude Agents SDK agent consumes it, investigates with custom tools, writes findings to a database, and exposes them through a web UI for back-and-forth chat.
- He draws on Anthropic's on-call kit repo (triage prompt plus a skill for investigating alerts) and a similar Google article, but stresses you must customize the prompts and wire in your own tooling.
- The lessons.md idea gives the agent persistent memory of past incidents and tribal knowledge, so repeat root causes — which are common — get diagnosed faster over time.
- In the live demo the agent triaged a bad deploy on its own, identified a payment configuration error and a spike of 500 errors, recommended rollback with reasoning ("errors began when traffic moved from this version, nothing else has changed"), and executed it after approval; error rates dropped immediately.
- Agent quality comes down to tool quality — read logs, read metrics, check deployment events, roll back — and writing the agent as code lets you scope permissions and validate that actions are safe.
- Beyond incidents, the agent can watch a rollout for a set window (e.g. "watch the next deployment for 5 minutes"), flag latency or error regressions, and offer to roll back — an idea he takes from OpenAI's software factory setup.
The core demo: automatic detection of a payment config error and 500 spike, a reasoned rollback recommendation, and the human approval gate before acting on production.
The agent is confident roll back is the right fix. Errors began when traffic moved from this version. Nothing else has changed since... So we can just ask the agent to do that. Roll back the change. And now it should offer us an approval.
The sharpest caveat in the video — he argues incident diagnosis needs human judgment paired with the agent's data-scraping speed, not full autonomy.
Although AI agents are really really powerful they do often jump to the wrong conclusions. If they see some kind of data or some kind of signal they maybe over-index on that signal too much... figuring out why a system is broken is actually quite complicated.
Humans are good at making decisions, but finding data and finding information at 4:00 a.m. when you're tired is actually quite stressful and incredibly difficult.
The quality of your agents often comes down to the quality of the tools you actually give the agents.
As we start deploying more and more code with AI agents, this kind of system becomes much much more important.