Replay QA for Software Factories

Your factory scales.
Bug reproduction doesn't.

An autonomous factory is not complete when it generates code. It is complete when a failed run creates evidence an agent can act on, and the next run verifies the fix.

Set up Replay QATalk to us

Start on your own, or talk through your trigger model, environments, and expected run volume with the team.

Runtime evidence

Turn a failed run into something a reviewer can judge.

A failed run needs more than a stack trace. A reviewer or agent needs to see what happened, understand the expected behavior, and decide what to do next.

Replay QA puts the reproduction and supporting runtime evidence in the report, attached to the full recording of the session where it broke. The reviewer does not have to reconstruct the failure from a diff.

Replay QA report showing a runtime bundle-loading issue, expected behavior, root cause, and supporting evidence

Runtime verification

The missing layer is runtime verification.

Code review and tests should stay in the pipeline. Replay QA adds the question they cannot answer on their own: did the changed user flow work in the running application?

LayerWhat it inspectsWhat it cannot tell you
AI code reviewThe diffWhether the changed flow works in the running app
Unit and integration testsThe journeys someone thought to assertThe flows nobody wrote a test for
Replay QAThe running app and its user journeysIt adds runtime evidence. It does not replace the other layers.

Keep the product knowledge. Stop maintaining the generic harness.

Keep the app-specific assertions that encode how your product should work. Let Replay QA handle the reusable runtime work around discovery, recording, triage, and evidence.

Integration

Your orchestration calls it. Your agents read the output.

1
Send a run from your orchestration. Use the REST API to set the target and trigger timing. Add it to pull-request checks, main-branch updates, or a scheduled verification pass.
2
Get reviewable runtime evidence back. Each failure returns a structured report with the recording and diagnostic context a reviewer or agent needs to decide the next step.
3
Use the same project across environments. Target dev, staging, production, localhost via reverse proxy, or a per-PR preview environment. Switch in settings or manage it programmatically.

Running it on every change? See Replay QA as a continuous testing platform.

Frequently asked questions

CodeRabbit, Qodo, and a cold-read model pass all read the diff. Replay QA runs the app. Same position in your pipeline, different layer. Most teams run both, because "the code looks right" and "the app works" are different questions.

Architecture questions? Talk to us.

Map Replay QA into your pipeline

Bring your trigger model, environments, expected run volume, and the decision you need a failed run to support. We will work through where Replay QA fits and whether it is the right setup for your team.

Set up Replay QATalk to us
Want your coding agent to do it?