Management Challenges of Multi-Agent Collaboration
When multiple AI agents work together, a core problem emerges: How to ensure each agent follows consistent rules? How to track "who did what"? How to avoid duplicate work or omissions like "I thought you didn't do it, so I did it too"? Traditional human project management tools are difficult to adapt to the automated nature of AI agents, while simple prompt constraints lack enforceability and auditability.
Core Philosophy of Immutable Rules
This project proposes an "Immutable Rules" system—once rules are established, all agents must follow them unconditionally, and all operations must leave traceable logs. Its core design principles include:
Append-only Changelog
The project uses a strict append-only mode to record each action, prohibiting modification or deletion of historical records. This design draws on the ideas of blockchain and Event Sourcing to ensure the auditability of system states. Agents cannot claim "we didn't do it" because logs will record the truth.
Mandatory Task Tracking
Each task must go through a complete lifecycle: Creation → Assignment → Execution → Verification → Closure. There is no concept of "implicit completion"; every state transition needs to be explicitly recorded. This eliminates common errors in multi-agent collaboration like "I thought you handled it".
Native Git Integration
Version control is not just a code management tool, but also a "single source of truth" for collaboration. The rule system is deeply integrated with Git workflows to ensure synchronization of code changes, rule updates, and task states.
Detailed Explanation of the Six Immutable Rules
The project defines six core rules that form rigid constraints on agent behavior:
| Rule | Essence |