openclaw-intelligence-core-public/docs/ARCHITECTURE_BOARD.md

Architecture Board

Goal

Build a local-first Openclaw agent that becomes more intelligent over time through:

• typed memory
• typed tool graph
• trajectory logging
• reward signals
• shadow meta-controller
• offline policy learning
• sacred eval gates

Hosts

• Mac Studio: hot-path inference
• openclaw: orchestration and live logging
• Unraid: offline learning, retrieval, replay, eval batch jobs
• Kimi: offline teacher only

Phase A

1. Typed trajectory schema
2. Reward signals
3. Replay buffer + policy stats
4. Tool graph
5. Uncertainty model
6. Shadow meta-controller