Capability Learning, Optimization, and Regression Control
Turn failures, corrections, and successful strategies into controlled capability improvements.
A focused view of boundaries, contracts, state, authority, failure paths, and tradeoffs drawn from this chapter.
Reconstruct and defend this chapter’s architecture.
Reconstruct the architecture, name each boundary, and defend the tradeoffs.
Open the source exercise
Take ten similar validation failures and five successful runs. Design clustering, causal diagnosis, three candidate remedies, an experiment, hard gates, promotion, and rollback. Explain why one remedy is the smallest durable change.
4. Tradeoffs and alternatives
Frequent optimization adapts quickly and creates version churn. Batch low-severity signals while escalating critical ones immediately. Automated prompt search can explore more candidates and overfit evaluators. Manual improvement is explainable and slower. Use controlled search with untouched holdouts and human promotion.
5. Current Mission Control Implementation
The current guide defines learning signals, clusters, improvement candidates, governed experiments, promotion recommendations, recursive-improvement boundaries, trust changes, and versioned configuration. It identifies prompts, skills, tools, context, routing, evaluators, and deterministic controls as possible targets.
It does not yet demonstrate a complete production optimization service, success-pattern analysis, prompt or tool experimentation, holdout protection, automated regression attribution, or promotion and rollback across the capability registry.
Review this chapter.
Challenge a claim, boundary, missing failure mode, unclear term, or unsupported evidence statement.
- Claim
- Boundary
- Failure
- Evidence