Governed Continuous Learning and Recursive Improvement

4. Tradeoffs and alternatives

Manual improvement is slower but easier to reason about. Fully automatic self-modification is fast and difficult to audit. The governed proposal model captures most learning value while preserving change control.

Offline evaluations are reproducible but may not represent production. Online canaries provide realism but expose users and systems. Use staged evidence and strict risk ceilings.

5. Current Mission Control Implementation

GitHub main contains Loop Engineering, graph workflows, context evaluations, meta-loop suggestions, verifier records, workflow-failure signal ingestion, and human conversion of accepted suggestions into governed WorkOrders and Tasks. The graph workflow has browser evidence for explicit dispatch, DAG visibility, failure containment, and terminal human approval boundaries.

Study commit 9d5f8e3 adds Phase 0 controls for governed continuous learning. It proves atomic ownership, pause/drain modes, budget admission, heartbeat, stale recovery, reasoned retry, cancellation, quarantine, independent verification, and operator-visible Task semantics in an isolated canary.

Continuous scheduling remained off. The preserved Research Lab queue was not mutated. Phase 1 still needs a governed source registry and ingestion policy. The broader continuous-learning plan remains proposed, and PR #64 is open. Mission Control therefore has a governed improvement substrate, not a self-operating learning factory.