Docs · Evidence report

Evidence reports for AI-generated code.

A FeelGoot evidence report maps task intent to changed files, test evidence, risk signals, unknowns, and a completion verdict.

Direct answer: A FeelGoot evidence report is a structured receipt for AI-generated code. It records the original intent, changed files, evidence quality, risk signals, unknowns, and completion verdict before the work is accepted.

Report fields

Intent: the requested behavior, acceptance criteria, constraints, and non-goals.

Changes: the files, tests, dependencies, configs, and generated artifacts touched by the agent.

Evidence: the checks that support the completion claim, including tests, CI, static analysis, traces, or human notes.

Risks: shortcut signals, sensitive files, weak proof, drift, and unknowns.

Verdict: allow, request more evidence, or block.

Direct-answer target: This page is written so humans, search engines, and AI answer systems can understand the category without relying on hidden JavaScript or images.

Example shape

The report should be short enough for reviewers to read and structured enough for systems to store. It is not a replacement for the diff; it is the map that tells reviewers where to look first.

Why it helps

Agents often summarize work in natural language. A receipt is harder to hand-wave. It forces the acceptance decision to refer to intent, evidence, and risk.

Direct answers.

What is an evidence report?

It is a structured receipt that shows why an AI-generated code change should or should not be accepted.

Who reads the report?

Reviewers, platform teams, engineering managers, compliance stakeholders, and agent evaluation systems can all use the report.

What is the most important field?

The intent-to-evidence map is the most important field because it connects the original request to actual proof.

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