| 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
- Extract Patterns - Mine execution data for recurring patterns
- RETRIEVE - Search ReasoningBank for matching trajectories
- JUDGE - Evaluate trajectory applicability in current context
- DISTILL - Compress and store new entries
- 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 tuningagents/adaptive-queen/ - Real-time adaptation
Tool Use
Invoke via babysitter process: methodologies/ruflo/ruflo-intelligence
