Controlled self-improvement for AI agents.
Most agents can improve.
Very few can improve safely.
AI agents that modify themselves tend to:
- drift over time
- lose working behaviors
- reinforce bad patterns
- become unpredictable
There is no built-in system for:
- tracking changes
- validating improvements
- reverting failures
Verified Capability Evolver introduces:
- structured learning logs
- gated promotion of changes
- rollback safety
- optional external verification
- Detect improvement or failure
- Log the learning
- Define evaluation criteria
- Verify results (optional)
- Promote or rollback
Without control, self-improving agents become unreliable.
This system ensures:
- improvements are intentional
- changes are measurable
- failures are reversible
This skill is part of a larger system:
- Skill Vetter → risk classification
- SettlementWitness → verification
- Capability Evolver → improvement
- Humanizer → transformation
- autonomous agents
- long-running workflows
- continuous optimization systems
- AI copilots with memory
Add this repository as a Claude skill.
ai-agents
self-improvement
agent-safety
verification
automation
Last updated: 2026-04-02