Operations | Monitoring | ITSM | DevOps | Cloud

Everyone wants an AI agent factory in 2026. Autonomous agents fixing bugs and shipping features while you sleep. I’ve been building toward that myself. But the error rates don’t support the fantasy. The best AI coding agents in the world fix about 50% of real bugs on SWE-bench verified. Half the time they fail. And AI-generated code produces 1.7x more issues than human-written code.

The Observability Gap shows up the moment you try to reproduce a production bug locally. Your traces tell you a request was slow. Your logs tell you which line printed. Neither tells you what was actually on the wire: the headers, the JSON body, the surprise field your client started sending last Tuesday. Until now, closing that gap meant SSHing to a node, attaching a debugger, or shipping a sidecar through change review.

Picture this. You’re standing up a new service. Cursor or Claude Code wrote most of the controller, and it calls a payment API your team doesn’t own. Now you need tests. The agent is gamely inventing the response shape from whatever OpenAPI doc you fed it (which is a year stale), and the WireMock stubs it just generated are guesses dressed up as JSON. Three weeks later production breaks, the test suite was green the whole time, and nobody knows where to start looking.

We’ve been building an AI agent that can take a production bug, find the root cause in captured traffic, write a fix, and validate it before a human reviews it. We call it Agent Factory. Last week we ran it on ourselves, against a real bug in our own production service. The first thing we did was get the workflow wrong.