Nathan Schneider bda868cb45 Implement LLM-driven governance architecture with structured memory ...

This commit completes the transition to a pure LLM-driven agentic
governance system with no hard-coded governance logic.

Core Architecture Changes:
- Add structured memory system (memory.py) for tracking governance processes
- Add LLM tools (tools.py) for deterministic operations (math, dates, random)
- Add audit trail system (audit.py) for human-readable decision explanations
- Add LLM-driven agent (agent_refactored.py) that interprets constitution

Documentation:
- Add ARCHITECTURE.md describing process-centric design
- Add ARCHITECTURE_EXAMPLE.md with complete workflow walkthrough
- Update README.md to reflect current LLM-driven architecture
- Simplify constitution.md to benevolent dictator model for testing

Templates:
- Add 8 governance templates (petition, consensus, do-ocracy, jury, etc.)
- Add 8 dispute resolution templates
- All templates work with generic process-based architecture

Key Design Principles:
- "Process" is central abstraction (not "proposal")
- No hard-coded process types or thresholds
- LLM interprets constitution to understand governance rules
- Tools ensure correctness for calculations
- Complete auditability with reasoning and citations

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

2026-02-08 14:24:23 -07:00

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