A coding-agent transcript records what happened in one session; it does not prove that another engineer can reproduce the change. For a bounded code spike, Harper Zhu proposes a stronger artifact: a narrowly scoped patch plus a replay script that checks and applies it, runs a characterization test, and records the result. The decisive check is whether those artifacts work from a frozen base in a second worktree or on another host—with no access to the original chat, agent, unsaved buffers, or hidden local state.
What a replay script proves—and what it does not
Zhu’s distinction is practical: “A chat transcript is a memoir of intent, not a receipt that another machine can cash.” The line is the author’s framing, not an independently established engineering standard. A transcript can explain an agent’s reasoning, but a replayable patch and test provide a concrete way for someone else to inspect and rerun the proposed change.
The method is a proposed ritual for a narrow, time-boxed spike, not a validated guarantee of correctness. A successful replay shows that the permitted artifacts can be used in the specified environment to apply a change and execute a test. It does not establish that the test covers every relevant behavior, that the patch is correct in all contexts, or that every hidden dependency has been found.
Prepare the test before prompting the agent
The starting point matters. Before the agent begins, define one narrow hypothesis, record the base commit, and prepare a clean worktree at that commit. The characterization test should already exist and fail on the base version; otherwise, the agent might be responsible for both the change and the only evidence that the change works.
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Write down which paths and resources the agent may use, capture a fingerprint of the source tree, and specify when to stop. These constraints make the later rerun meaningful: another person should be able to distinguish the intended patch from unrelated or untracked changes.
Zhu’s example uses a folder of spike artifacts, including a hypothesis file, clock settings, a test fixture, and eventual replay files such as replay.sh, patch.diff, and RESULT.json. The sample pagination test and paths are illustrative, not a report of an actual incident. The sample SPIKE_MINUTES=90 is an operator-chosen example, not a vendor or model limit or a measured recommendation.
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Build a replay that separates patch checks from test checks
The proposed script changes to the repository root, verifies that the patch file exists, checks whether the patch applies, applies it, runs the pre-existing fixture with pytest, and writes a result record. The essential point is that patch applicability and test behavior are separate checks.
#!/usr/bin/env bash
set -euo pipefail
cd "$(git rev-parse --show-toplevel)"
patch_file="path/to/patch.diff"
test_file="path/to/test_fixture.py"
result_file="path/to/RESULT.json"
if [[ ! -f "$patch_file" ]]; then
echo "Missing patch: $patch_file" >&2
exit 1
fi
git apply --check "$patch_file"
git apply "$patch_file"
pytest -q "$test_file"
printf '{"replay":"passed"}n' > "$result_file"
This is an unexecuted template, not a tested script. Adjust paths, test invocation, and result format for the repository. The example also assumes that required tools and dependencies are available; the replay should make those prerequisites explicit rather than silently relying on a local environment.
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Git’s official git apply manual says --check checks whether a patch is applicable and turns off applying it. Thus a passing check means only that the patch can be applied in that context. It does not run the test, establish that the test is adequate, or show that the resulting diff is correct. Review the diff and evaluate test results separately.
Run the second-worktree or second-host check
- Check out the recorded frozen base in a second worktree or on another host.
- Copy only the permitted replay artifacts and any explicitly allowed fixtures or dependencies.
- Run the replay script without access to the original agent, conversation, notes, editor buffers, or unlisted local state.
- Inspect the resulting diff, test output, and result record; compare them with the written hypothesis and scope.
If replay depends on the old chat, an unsaved buffer, or agent assistance, the proposed hypothesis has failed: the artifact is not independently replayable under the stated conditions. A different virtual environment or host setup may be legitimate if it is declared and reproducible; an undocumented dependency is not evidence of a clean rerun.
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Keep safeguards proportional and claims precise
Zhu also sketches a watchdog and an inventory checker. These are unexecuted examples, and the checker is described as a small proposed helper—not a validated, complete containment mechanism. Teams can use such checks to make scope visible, but should not claim they guarantee that no undeclared files or dependencies influenced a run.
Similarly, a passing replay is one piece of evidence, not proof of broad correctness. The test’s quality, the patch review, the frozen starting point, and the reproducibility of required dependencies all still matter. The proposal reports no controlled evaluation, benchmark, or demonstrated improvement in engineering outcomes.
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When this ritual fits—and when it does not
The workflow is aimed at bounded changes where a specific hypothesis and an existing failing characterization test can be defined in advance. It is a poor fit for exploratory product design, where the desired outcome may evolve during work, and for incidents that require live production traffic. It may also be redundant for teams whose trusted CI gate already runs on every agent patch. Those are scope judgments from Zhu’s proposal, not measured comparisons of verification methods.
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