See what your coding agents actually shipped.

Coding agents now open real pull requests, refactor real modules, and touch production paths - but most teams only see the chat transcript, not the consequences. A run that "looks successful" can still introduce a regression that surfaces days later as an incident or a quiet revert.

AI agent observability closes that gap. Instead of trusting a green checkmark at the end of a run, you trace every agent-authored change to the pull request it produced and the production behavior that followed. That means you can answer the questions that actually matter: what did this agent ship, did it get reviewed, and did it hold up?

FeatureFactory builds this view from the artifacts you already have. If you want the fuller picture of attribution across the lifecycle, start with how we measure AI-generated code.

Raw agent logs tell you what happened inside a model call. They do not tell you whether the feature worked. Observability worth having connects the two - mapping each run to its diffs, its reviewers, its CI results, and the incidents or reverts that came after.

With that chain in place, you can compare agents fairly. Which tool ships code that survives review with fewer round-trips? Which one drives up change failure rate? You can track this per agent and per repo, then fold it straight into your existing DORA metrics and cycle time analytics.

This is the "measure" half of a developer-led factory: plan the work, build it with agents, and observe what actually landed. Explore the full loop on the measure product page.