recursive-self-improvement

Agent self-improvement can be safely constrained using metric gates, sandboxed execution, and diff validation. This approach enforces bounded self-modification through pre-commit CI gates and circuit breakers, ensuring autonomous workflows remain stable and production-ready.

Structuring agent memory for meta-learning requires three-tier versioned memory: episodic, semantic, and procedural. This configuration supports decay, compaction, and consolidation routines, enabling agents to archive successful patterns and prevent context pollution during skill evolution.

Metric-gated self-improvement works by running generate, execute, evaluate, and refine cycles with automated evaluation and meta-evaluation. It tracks orthogonal metrics like pass rate and token cost, applying anti-Goodhart checks and blind A/B verification to compound gains safely.

Automated prompt compilation and self-play are supported through DSPy-style patterns and controlled adversarial testing. This generates continuous training signals via bug-injection and fix cycles, enabling agents to optimize prompts and debug code within isolated self-play pipelines.

Skill pruning and version control manage context pollution through a formal promotion pipeline and an archive-first prune lifecycle. By implementing monitoring and verification gates, agents can safely retire outdated skills while preserving validated procedural knowledge.

Autonomous self-modification workflows should be avoided when environments lack sandboxed execution or constitutional immutables. Without diff validation and circuit breakers, running unconstrained generate-execute-evaluate loops risks introducing regressions and unsafe code modifications.