The review problem
Traditional code review assumes a human author understands the task and can explain tradeoffs. AI-generated code can look coherent while hiding mismatches, shortcuts, or missing test coverage.
Reviewers need a compact risk map that starts from the original intent and shows what evidence exists.
FeelGoot's review model
Instead of asking reviewers to reread an agent’s confident summary, FeelGoot generates a receipt: intent mapping, changed-file analysis, evidence quality, risk signals, and a completion verdict.
The result is a review queue where humans can spend less time reconstructing context and more time making the acceptance decision.
What gets flagged
Files that changed without a clear relationship to the task.
Tests that pass because important behavior is mocked, skipped, or replaced by a hardcoded path.
Summaries that claim full completion when the evidence only supports partial completion.
Risky changes in authentication, authorization, billing, data migration, infrastructure, or customer-facing flows.