The new review burden
Coding agents can create convincing diffs, tests, and summaries. That changes the reviewer’s job from reading human intent to verifying an agent’s claim.
The agent may be correct, but the team still needs evidence. A completion claim should be accepted only after the evidence supports it.
A practical verification checklist
Start with intent: restate the requested behavior, constraints, acceptance criteria, and non-goals.
Map changes: identify which files changed and why each change connects to the task.
Inspect tests: decide whether tests exercise real behavior or merely create a green result.
Look for shortcuts: stubs, mocks, skipped tests, hardcoded success, broad catch blocks, and narrow fixtures.
Classify risk: auth, billing, infrastructure, data, migrations, and customer-facing flows require stronger evidence.
How FeelGoot fits
FeelGoot turns the checklist into a repeatable evidence report. Instead of trusting an agent’s narration, reviewers get a compact receipt: intent match, evidence strength, risk signals, unknowns, and a completion verdict.
That receipt can support AI code review, CI gating, agent evaluation, and higher-assurance engineering workflows.
Bottom line
AI-generated code should not be rejected by default or accepted by default. It should be verified. The future of AI software engineering is not only faster code; it is faster code with evidence.