Evaluating self-improving software after release

Evaluating self-improving software after release requires treating each proposed change as a controlled release decision, with a clear baseline, stable success criteria, comparable tasks, and evidence that required checks were completed. Measures such as speed, issue volume, or apparent cost can be misleading if task difficulty changes, tests are skipped, or engineer review and repair work is omitted, so evaluations should separate output quality from agent explanations and document unacceptable regressions in advance. Teams should record workflow versions, models, tools, repository and environment conditions, use representative and held-out tasks, distinguish excluded or blocked cases from implementation failures, and track human effort across preparation, review, repair, and follow-up. Post-deployment monitoring should assess whether improvements generalize to new work while accounting for changes in task mix, release cadence, staffing, and connected-system coverage, rather than attributing every trend to automation. A durable release record should identify the change, evidence, remaining uncertainty, human corrections, blocked checks, ownership, and rollback signals, enabling teams to retain, revise, or cautiously roll out candidates based on defensible results.

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