---
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`