Feedback Loop
Self-EvolutionSelf-evolution engine that analyzes execution outcomes, identifies patterns, and drives continuous improvement across all agents and skills in the platform.
Core Functions
- Collect: Gather execution feedback from all operations
- Analyze: Identify success patterns and failure modes
- Learn: Extract actionable insights from outcomes
- Recommend: Generate improvement proposals
- Track: Monitor evolution metrics over time
- Propagate: Share learnings across the agent ecosystem
Feedback Architecture
The feedback flow follows a structured pipeline:
- Execution: Agent/skill performs task
- Collection: Capture outcome, duration, errors, satisfaction
- Analysis: Pattern recognition, root cause identification
- Learning: Update knowledge base, refine heuristics
- Improvement: Generate recommendations, modify behaviors
Pattern Detection
- Success Patterns: What conditions lead to successful outcomes
- Failure Patterns: Common failure modes and root causes
- Trend Detection: Changes in performance over time
- Correlation Analysis: Relationships between factors and outcomes
Learning Categories
- Best Practice: Proven successful approaches to replicate
- Anti-Pattern: Common failure modes to avoid
- Optimization: Efficiency improvements discovered
- Insight: Understanding about system behavior