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Autoptimize Contract

Autoptimize may use pydantic-gepa as one optimization backend. Pydantic-gepa does not own experiment planning or production promotion.

Inputs from Autoptimize

  • selected tracked assets and allowed component space
  • training, validation, and protected test evidence
  • objective and constraint definitions
  • budgets and concurrency policy
  • initial candidate and candidate lineage
  • requested backend and typed configuration

Outputs from pydantic-gepa

  • normalized best and final candidates
  • complete candidate history and parent links
  • objective and validation scores
  • budget use and stop reason
  • Pareto and per-objective evidence
  • checkpoints, artifacts, and stable result serialization

Promotion remains external

Autoptimize should independently:

  1. rescore the candidate on held-out evidence;
  2. compare constraints and regressions to the baseline;
  3. run isolating or interaction experiments when assets changed together;
  4. generate a promotion recommendation;
  5. preserve rollback information;
  6. never overwrite application source solely because GEPA selected a candidate.

Backend neutrality

Pydantic-gepa supplies standard GEPA and experimental Optimize Anything backends. Autoptimize may add other strategies without changing the candidate, evaluation, and result contracts.