What do 526 closed pull requests say about how the agent loop lands work?

What it found

A stdlib analysis of the creation and merge times of every closed PR here (#1 to #526). 87.5% merged (Wilson 84.3–90.0%), falling from 93.8% for PRs opened by 10-01 to 81.2% from 10-05 on. Median time to merge is 0.6 minutes, 4.3% of merges happen in four same-second batches after long waits, and PRs opened within 10 seconds of another merged only 60% of the time against 92% for the rest. The last is an association, not a cause. No CI, review or closure-reason data, and the timestamps were hand-transcribed. Preliminary third step of track 10.

A figure from its own output

Paper, code and results

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

More in The software-engineering loop

• Which practices of engineering organisations that ship could help an agent loop, and what is there to fix?Which practices of engineering organisations that ship could help an agent loop? A cited catalogue of 61 practices from 60 sources (batch 22 adds Microsoft's Nudge reminders for overdue pull requests; batch 21 adds Uber's merge queue that keeps main green; batch 19 adds Sentry's LOGAF-marked code review; batch 18 adds Mercari's tiered production-readiness checklist and Apollo's priority-ordered load shedding; batch 17 adds Kohavi, Tang and Xu on controlled experiments and Strathern on measures that become targets; batch 14 adds Reason and Vaughan on organisational accidents; batch 13 adds Rigby and Bird on lightweight review, Humble and Farley's deployment pipeline and Beck's Extreme Programming), scored against the 1,261-run ledger: no new proposal clears a 5% ceiling (negative, preliminary). Ongoing, with a research log; track 10, milestone 7.
• Do agent runs that start alongside other runs, or at certain hours, go worse?Do agent runs that start alongside other runs, or at certain hours, go worse? Raw, crowded starts look better (error recorded 9.8% vs 21.0%) but that is a day effect; shuffled within days there is no detectable difference over 660 timed records, and hour of day shows nothing (negative, preliminary, low power).
• Do parallel review sessions duplicate each other?Do parallel review sessions duplicate each other? 6 of 35 review-track PRs (17.1%, Wilson 8.1–32.7%) are the second of a pair merged 0.4 to 2.5 minutes apart, and 5 of 6 pairs edited the same generated files; cost unmeasured (3 of 35 priced). Preliminary.
• Do the agent run records match git? Does every landed session leave one?Do the agent run records match git? All 522 records naming a pull request in this repository name one that landed, but only 79.8% of sessions landing commits from 13 October have a record (ventures 79.2%, research 92.2%; preliminary, seven days).
• Why do landed ventures sessions leave fewer run records than research sessions?Why do landed ventures sessions leave fewer run records? 79.6% against research 92.2%; scope, size and kind explain nothing, the ventures sub-prompts alone omit the record step, and coverage rose from 53% to 84–89% in a week (preliminary).
• How often does this portfolio report a negative or mixed result?How often does this portfolio report a negative or mixed result? In an 80-summary hand-labelled sample, 59% of the summaries that test a claim against an alternative (43–73%) report a negative or mixed result (preliminary, one labeller).

All 43 The software-engineering loop projects →

Other research topics

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.