Verify every PR in production.
AI coding tools moved the bottleneck from writing code to verifying it. Firetiger reads every PR, watches the deploy, and reports whether the change behaved as expected — so verification scales with PR volume instead of with reviewer headcount.
Why AI coding changes the verification problem
Manual post-deploy checking does not scale past a small number of deploys per day. Teams adopting Cursor, Claude Code, Codex, and similar coding agents typically report PR volume doubling within months. Adding reviewers moves the bottleneck rather than removing it. Adding pre-merge gates slows velocity, which is the advantage AI tools were supposed to deliver.
The structural response is to move verification from before-merge to after-deploy and make it automatic, per PR. That is what Firetiger does: one monitoring plan per PR, one verdict per deploy.
What this looks like in practice
Reads each PR's diff, including AI-authored ones
Firetiger doesn't care who wrote the code. It reads the diff and PR description, identifies which services and code paths the change touches, and generates a monitoring plan tailored to that PR.
Watches production against the plan
After the PR deploys, Firetiger watches the rollout across staging, canary, and production. The plan compares post-deploy behavior to the pre-deploy baseline for the slices the change affects, not against a generic threshold.
Catches semantic bugs static testing misses
AI tools often produce code that compiles, passes tests, and looks fine in review — but interacts poorly with real production data shapes. Verification against production behavior surfaces these within minutes of deploy.
Hands the verdict to your coding agent
When a regression is detected, Firetiger posts the verdict on the PR with the affected scope, suspected code path, and supporting evidence — structured for Cursor, Claude Code, Codex, or whichever agent you use to write the fix.
Closes the loop with your coding agents
Firetiger's verdict is structured for downstream automation. When a regression is detected, the report on the PR identifies the affected scope, the suspected code path, the change author, and the recommended action. Cursor, Claude Code, Codex, and your internal agents can read that report and start working on a fix without rebuilding context.
Want the deep dive?
The educational reading sequence behind this approach lives in the Learning Center: Verify AI-Generated Code in Production — reading sequence.