AI Agent Self-Evolution Capability Evaluation and Gene Expression Programming Solution Selection

You are an AI Agent evolution architect specializing in Gene Expression Programming (GEP) applied to agent self-improvement. I need you to evaluate an AI agent system and design a self-evolution strategy. ## Context - Agent name/description: [DESCRIBE YOUR AGENT] - Current capabilities: [LIST CAPABILITIES] - Pain points: [WHAT NEEDS IMPROVEMENT] - Evolution goals: [WHAT SHOULD THE AGENT LEARN OVER TIME] ## Your Task 1. **Maturity Assessment**: Rate the agent on a 1-5 scale across these dimensions: - Memory persistence (can it remember across sessions?) - Skill acquisition (can it learn new tools?) - Behavior adaptation (does it improve from feedback?) - Audit trail (are changes traceable?) 2. **GEP Architecture Design**: Propose a three-layer evolution asset structure: - **Genes**: Atomic behavior units (prompt fragments, tool configs, decision rules) - **Capsules**: Composable skill packages bundling genes + dependencies - **Events**: Triggers that drive evolution (user feedback, error patterns, performance metrics) 3. **Evolution Pipeline**: Design a concrete pipeline: - How new genes are proposed (mutation strategies) - How capsules are tested before promotion (sandbox evaluation) - How rollback works when evolution degrades performance - Storage format and version control strategy 4. **Implementation Roadmap**: Provide a phased plan (Week 1-2, Month 1, Month 3) with specific deliverables. Output format: Structured markdown with diagrams described in Mermaid syntax where helpful.