Iterating towards optimal agentic performance.
Closes the loop. metrics.py: fleet metrics (infra-overhead share, error rate, schema-thrash, token percentiles, success) + persisted baseline trend. effect.py: before/after per-pattern effectiveness with an improved verdict per metric. measure entrypoint with trend + --since effectiveness + JSON. Recorded pre-fix baseline: 27 sessions, overhead median 11.7%, error rate 0.96, schema-thrash 8. 13 new tests; suite 139/139. Capture->Detect->Curate->Distribute->Measure complete. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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| docs | ||
| session_memory | ||
| tests | ||
| workplans | ||
| .custodian-brief.md | ||
| .gitignore | ||
| AGENTS.md | ||
| INTENT.md | ||
| LICENSE | ||
| README.md | ||
| SCOPE.md | ||
Iterating towards optimal agentic performance.