Do agent runs that record a deviation from the brief cost more or end worse?

What it found

Of 1,564 run records, 203 (13%) list a deviation from the brief, mostly review (59%) and ventures (27%) runs. Within track, deviating runs take about 1.6× as long and cost about 1.2× as much (stratified permutation p < 0.001), but the cost gap rests mainly on the long process track (1.07×, p = 0.052 without it), and deviating runs end partial 5 points more often. Observational and self-reported: preliminary.

Paper, code and results

• README and method
• Experiments and results
• Code
• Tests

More in The software-engineering loop

• How well does a run's tool-call count predict what it costs?How well does a run's tool-call count predict its cost? On 882 priced run records cost is about $0.03 per call, a log-log fit has R² 0.91 and a held-out median error of 9%; the pooled slope 1.22 is partly group mixing (1.10 within Sonnet). Preliminary.
• Where does an agent run's cost go, and why does it grow faster than its tool calls?Where does a run's cost go? On 893 run records with per-token-kind usage, cache reads are a median 45% of the API-equivalent cost and 1-hour cache writes 41%, output only 13%; the starting context (about 60,000 tokens) is re-read on every request, which makes cost grow faster than linearly in tool calls (preliminary).
• Do agent runs that add no tests end worse?Do agent runs that add no tests end worse? 84 of 621 counted run records add none; within track they end partial 4/84 against 16/537 (+2.6 points, p = 0.23, no detectable effect) and cost less, probably because they do less. Preliminary.
• Which kind of recorded deviation from the brief predicts a partial outcome?Which kind of recorded deviation predicts a partial outcome? Of 213 run records with a deviation, tooling failures end partial 22.6 points more often than runs with none (track-stratified p=0.001); skipped steps (+1.3) and switched tasks (+1.3) do not. Keyword classes, observational, preliminary.
• How complete are the agent run records, field by field?How complete are the agent run records, field by field? Of 1,362 records, cost, tokens and wall time are present on 47% (0-7% on 13 to 15 October, 61-82% since), merged on 4.6%, and 18.5% of success runs name no pull request (preliminary; presence, not correctness).
• Does a bigger agent run buy more output?Does a bigger run buy more output? Across 157 priced run records that report adding tests, cost and tests added rank together pooled (Spearman 0.57) but weakly within research runs (0.19), and the dearest third yields the fewest tests per dollar; preliminary, tests added is a crude self-reported proxy.

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Proper scoring & elicitation28Effort & peer prediction9Verification games & slashing19Detection & tolerances14Markets as routers13Prediction-market design9Paying contributors8Decentralised training systems23Digital twins & sim-to-real164LLM chip design loops40Robot manufacturing loops53Ambient AI assistant46Self-replicating manufacturing34Simulation-first design64Engineered clothing45Task-specific AI accelerators40Algorithmic chip-design loops42Open-source purpose-built robots44The software-engineering loop43Adversarial human imitation41Open-source residential construction41Autonomous multi-angle capture34Sensing, imaging and 3D31Energy supply and dense energy storage39Robot societies among people31Agentic co-design of low-cost robots27Laundry-hanging robots25Machine learning on qualitative data23Robots on the building site27Civilizations & group psychology1History & human sciences1

Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.