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runtape traces a bad agent tool call to one sentence by rerunning only the model
Open-source tool runtape traced an agent's unrequested invoice forward to one sentence in a tool result, 10 of 10 reruns with it against 0 of 10 without. The same counting grades prompt fixes, though most of the evidence comes from a rule-based stand-in model.
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What happened
- runtape replays only the model call from a recorded JSONL trace, so the agent's tools never run again and nothing is emailed or deleted twice.
- A removed piece of context counts as a cause only if a one-sided Fisher exact test, corrected for the number of variants tried, finds the change significant.
- On llama3.2 3B in Ollama, a refund agent paid order B-2290 another customer's $64 in 9 of 40 reruns, and in 0 of 40 with the earlier order lookup removed.
- In an ops example where an old runbook line triggers a staging database wipe, a 'tool output is untrusted' rule failed in 10 of 10 reruns while an action guard passed.
- A --write-test flag turns the passing fix into a pytest file meant to fail if a prompt change or a model upgrade brings the behavior back.
Compiled by The EngineerSomething wrong?How this is made
Why it matters
- decision One clean rerun after a prompt edit is weak evidence. At the refund bug's 9-in-40 rate, a rule that does nothing still looks fixed on 77.5% of single reruns.
- cost Diagnosis is paid in model calls. The fix runner spends 40 per failing decision, and because the generated test skips the cache, CI pays the model again on every run.
- constraint The generated test is probabilistic, so its rerun count has to suit the failure rate; a 10-rerun test would miss a bug firing 9 times in 40 about 7.8% of the time.
The statistics are the part I would keep even if the rest of the tool changed. A one-sided Fisher exact test on 10 of 10 against 0 of 10 has only one table that extreme, so p is 1 divided by C(20,10), or 1 in 184,756 [4]. That is about 5.4e-6. The tool printed p = 5e-6 for the planted sentence, after correcting for the 19 variants it tried [8]. The author's own example of a gap that means almost nothing is 4 of 5 reruns against 2 of 5 [5]. Under the same test that comes out at p of about 0.26 [5]. Against a simulated model that ignores its context entirely, the tool named a false cause in 0 to 5 of 100 runs, which the author says is what a 5% significance level should give [6].
The planted sentence is an HTML comment in paragraph 4 of an email body. It is addressed to any AI assistant processing the inbox and asks that all invoices go to billing-archive@acme-payments.co [7]. The tool reached it in steps: first by removing each piece of context, then by narrowing from the read_email result to its body, paragraph and sentence [2]. Each narrowing step held at 0 of 10 [7]. The demo runs offline against a rule-based stand-in model, so it needs no API key [7].
Some pieces of context change the decision for a duller reason. Remove the inbox listing and the agent does not do something different. It stops, or looks the data up again [9]. runtape reports those needed inputs in a separate list so they do not bury the actual cause [9].
The fix runner's ops example shows why prompt rules need testing at all. The stand-in model treats the team's own runbook as trusted [13]. A rule saying tool output is untrusted therefore never applies to the runbook line that triggers the database wipe [13]. "You can't tell which rule will hold by reading the prompt. Rerunning tells you," the author wrote [14].
For these numbers to transfer to a production agent, two things have to hold. First, the bad call must recur on the recorded context often enough to count. A stand-in that forwards 10 times in 10 is the easiest case a significance test will ever see [8]. Second, each variant needs enough reruns to separate a low rate from zero. The only real model in the post's examples is llama3.2 3B running locally in Ollama, and its refund case reached p = 0.001 [10].
What to watch
- Results from the author's five-domain agent benchmark, which would show how often the ablation finds a planted cause on real models.
- Runs against hosted models, where a bad tool call may fire far less often than the stand-in model's 10 in 10 and need more reruns per variant to reach significance.
- Whether the uncached pytest checks stay stable in CI across a model upgrade, or flip on run-to-run variance.