self-optimization

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name: self-optimization description: SONA self-optimizing neural architecture with ReasoningBank trajectory learning, EWC++ anti-forgetting, and reinforcement learning feedback loops. allowed-tools: Read, Write, Edit, Bash, Grep, Glob, WebFetch, WebSearch, Agent, AskUserQuestion graph: domains: [domain:software-engineering] skillAreas: [skill-area:agentic-loops, skill-area:orchestration-loop] workflows: [workflow:feature-development] topics: [topic:developer-experience] roles: [role:tech-lead, role:backend-engineer]

• Improving routing and agent selection over time
• Adapting to new project patterns without forgetting old ones
• Building cross-session intelligence

SONA Cycle

1. Extract Patterns - Mine execution data for recurring patterns
2. RETRIEVE - Search ReasoningBank for matching trajectories
3. JUDGE - Evaluate trajectory applicability in current context
4. DISTILL - Compress and store new entries
5. Adapt - Update weights with EWC++ regularization

Anti-Forgetting (EWC++)

• Elastic Weight Consolidation prevents overwriting previously learned patterns
• Fisher information matrix tracks parameter importance
• Configurable regularization penalty for new adaptations

RL Algorithms

Q-Learning, SARSA, PPO, DQN, A2C, TD3, SAC, DDPG, Rainbow

Agents Used

• agents/optimizer/ - Performance tuning
• agents/adaptive-queen/ - Real-time adaptation

Tool Use

Invoke via babysitter process: methodologies/ruflo/ruflo-intelligence