/self-optimization

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`