Operations | Monitoring | ITSM | DevOps | Cloud

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.

This is Part 2 of the AI Software Factory series. In Part 1, we established that the Agile methodology is buckling under the weight of “elastic code.” When AI agents can generate functionality in seconds, two-week sprints and manual task management become organizational bottlenecks. We introduced the concept of the AI Software Factory: a shift from managing human tasks to managing business intent through a “Funnel of Increasing Trust.” But a factory requires infrastructure.

Before I ever ran Claude in my terminal, I thought I already understood AI tools pretty well. Like most people, I had used ChatGPT, Google Gemini, and Perplexity for everyday tasks. Such as helping with schoolwork, organizing ideas, summarizing information, or getting through something faster when time was tight. They were useful, but they still felt separate from how real work happened.