Did your coding agent actually run the tests it says passed? Treat every closing summary as a set of claims to verify—not as evidence. Check the command and output, rerun the relevant check against the final code, compare the diff, and verify Git or pull-request actions separately. Then write the devlog to match exactly what those records establish.
What counts as evidence for an AI coding agent’s claim?
“Tests pass,” “bug fixed,” “build works,” “committed,” and “pushed” describe different outcomes. A conversational statement alone establishes none of them. Match each claim to evidence capable of supporting it, and state the scope and timing of that evidence.
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| Claim | Evidence to check |
|---|---|
| Tests passed | The actual test command, its output and exit status, which tests it selected, and a fresh run on the final code. |
| A bug was fixed | The relevant diff plus a reproduction, regression test, or other check that addresses the reported behavior. |
| The build works | The actual build command and output for the relevant project and configuration, run against the final code. |
| Changes were made | The diff against the stated base, including files changed and any relevant generated files. |
| Changes were committed, pushed, or opened as a pull request | Separate local Git evidence for a commit from remote or pull-request evidence for delivery. |
Keep a claim unverified or inconclusive when its evidence is absent, ambiguous, stale, or incomplete. A diff shows what changed, for example, but does not by itself show that the behavior is correct.
How do I check what the agent actually ran?
1. Break the summary into atomic claims
Split compound sentences before checking them. “I fixed the bug, added tests, and pushed the branch” contains at least three claims, each requiring different evidence. For a numerical claim, retain the exact quantity, unit, denominator, measurement or command source, and relevant time or version. Do not turn vague wording such as “many tests passed” into a count.
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2. Inspect the transcript’s command, output, and ordering
Find the actual invocation and its output in the session record. Check which tests the selector matched, whether any tests ran, and whether a command chain could hide a failure. A zero exit status alone may not establish that tests were selected or executed. Transcript parsers can also have gaps: Backcheck documents that transcripts may omit exit codes and that its runner-specific output parsers can return an inconclusive result when success is ambiguous (Backcheck).
3. Rerun the relevant check on the final code
Use the project’s real test or build entry point in the relevant worktree. Record the exact command, output, and exit status, and confirm the invocation did not match zero tests. If you run only a subset, identify it as a subset rather than saying the entire suite passed. A pass from before a relevant final edit may not describe the code being logged; audit logic described by Backcheck counts a pass only when it follows the last source edit and precedes the claim.
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4. Compare the diff with the claim
Read the changes against the stated base. Look for unmentioned files, generated files, skipped or weakened assertions, and edits made after the last passing run. Connect the diff to an appropriate test, reproduction, build, or other check; the existence of a change is not proof of a fix.
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5. Verify repository and delivery actions separately
A local commit, an upstream branch, a successful push, and an opened pull request are distinct states. Check local history and branch/remotes for local Git claims, and use remote or pull-request evidence for claims about delivery. Agent-verify describes checks against local Git or GitHub CLI state where available, and treats missing dependencies or test commands as inconclusive rather than automatically treating a claim as false (Agent-verify).
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How should the devlog phrase what happened?
Make the sentence reflect the evidence’s scope and timing. If that is what the record shows, write: “Ran pytest tests/auth; it reported 18 passed. Reran it after the final edit.” Do not expand a module-level result into “all tests pass.” If a remote push could not be checked, say it was not verified. Claim a bug is fixed only when a reproduction or other appropriate check supports that conclusion. Never infer dates, counts, coverage, files changed, or completion state from the agent’s summary.
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A 2026 preprint, “Between the Commits,” reports that 14.3% of AI code-generation events in its study contained errors later caught by the AI-authored test suite. The result describes one 21,000-line Python tool built entirely with Claude AI, with its own test suite and development history—not a population-wide error rate for coding assistants (Between the Commits).
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In that same project-specific dataset, the authors report interactive-response accuracy of 94.3% for reporting or verifying a fact, 91.1% for explaining an existing mechanism, 89.3% for diagnosing a root cause, and 79.4% for proposing a design fix. The studied project and corpus comprised 210 commits, 25 sessions, and 678 user instructions. These figures describe that dataset and those categories; they do not establish how often agents generally misreport completion or provide a cross-tool ranking.
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Can verification tools prove an agent’s summary is accurate?
Tools can help organize evidence, but their stated coverage and limits matter. Backcheck describes auditing finished Claude Code transcripts with runner-specific output parsing. Agent-verify describes test, file, and Git or pull-request checks with an inspectable receipt, while documenting integration limits. EviGate describes deterministic comparison of observed tool events with declared claims, but its repository also documents that shell-based file edits can escape one file-scope detector (EviGate). These are project-described capabilities, not independent comparative-accuracy results. Check which agents and transcript formats a tool supports, whether it inspects current repository or remote state, how it handles ambiguity, and whether it runs after a session, as a local gate, or in CI.
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The Git AI Standard v3.0.0 addresses a related but narrower need: an authorship-log format attached through Git Notes that maps code lines and conversation threads to commits. It says, “Authorship logs provide a record of which lines in a commit were authored by AI agents, along with the conversation threads that generated them.” That can help establish provenance, but authorship attribution does not establish that code works or that a task is complete (Git AI Standard).
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