AI Autonomous Evolution

Problem

Long-running AI agents can execute tasks without closing the loop: temporary files accumulate, memory grows noisy, tool calls become inefficient, and repeated failures stay untracked. Directly applying self-proposed fixes can also cross safety boundaries. This skill turns maintenance into an explicit, triggerable workflow rather than relying on ad hoc human reminders.

How It Works

It follows a Hermes four-layer loop across 12 modules, including pending-memory consolidation, session and delivery accounting, cache cleanup, capability-gap analysis, SKILL.md distillation, regression checks, and PR-based review items. Its 14 evolution directions cover MEMORY.md capacity control, intent clarification, context compression, error rollback, tool orchestration, GEPA prompt self-optimization, and runtime defenses such as input filtering, decision sandboxing, and output baselines. The process emphasizes record, verify, then apply: generated skills or optimizations must become PR entries and require user confirmation before taking effect. Execution is bounded to 12 minutes, with timeout priority for critical maintenance and logging.

Boundaries

It is intended for agent workflows with file, log, memory, and skill management capabilities. It does not replace core security rules or guarantee risk-free automatic behavior changes. High-risk edits should remain under human review, and missing reference documents should not be inferred as implemented features.