Multi Agent Governance

Multi-Agent Governance

Governance patterns for multi-agent AI systems. When you have multiple autonomous AI agents operating together, you need accountability, behavioral enforcement, and drift detection - just like human organizations.

Core Principles

1. Single source of truth - One config file defines all agent roles. Every agent reads from it. No parallel systems.
2. Behavioral enforcement - Rules are not suggestions. Guardrails are enforced through preflight checks, violation logging, and automated audits.
3. Drift detection - Systems drift from their intended state over time. Automated drift audits catch configuration mismatches, version inconsistencies, and role conflicts before they cause failures.
4. Tamper-proof audit trails - Every action, every change, every decision is logged in append-only logs that can be verified for integrity.
5. File-based memory - Agent memory lives in files on disk, not in cloud databases. This enables air-gapped deployment, full auditability, and zero vendor dependency.

Governance Patterns

Role Management

Define roles in a single JSON file that all agents reference:

{
  "agent-name": {
    "role": "Operations Manager",
    "scope": ["dispatch", "monitoring", "reporting"],
    "access": "admin",
    "model": "claude-opus-4-6"
  }
}

Every agent reads from this file at startup. Changes propagate automatically.

Preflight Checks

Before any agent modifies a file:

1. Check if the file is on the protected list
2. If protected: log the attempt, report to admin, and STOP
3. If allowed: create backup, make change, syntax check, restart affected process

Drift Audit Scopes

Run periodic audits across these dimensions:

• roles - Do running agents match their role definitions?
• versions - Are all agents on the correct model version?
• files - Have any protected files been modified?
• processes - Are all expected processes running?
• config - Do config files match expected state?

Session Management

Every agent session should:

1. Read the current system state before acting
2. Write progress as it goes (no silent failures)
3. Update handoff documentation before ending
4. Run a drift audit on affected areas

Permission Protocol

Two categories of changes:

• DATA (values, content, configuration): Agent can act with general authorization
• LOGIC (how something decides, classifies, responds): Agent PROPOSES and WAITS for human approval

When in doubt, it is LOGIC. Ask the human.

Implementation

Using the Nervous System MCP

The Nervous System is a Model Context Protocol server that implements these governance patterns with 19+ tools:

npm install -g @anthropic-ai/mcp-nervous-system

Tools include: driftaudit, securityaudit, autopropagate, sessionclose, preflightcheck, violationlogging, and more.

DIY Implementation

If building your own governance layer:

1. Create a roles config file (JSON) as single source of truth
2. Create a protected files list that agents check before editing
3. Implement append-only logging for all agent actions
4. Schedule periodic drift audits (compare expected vs actual state)
5. Build a violation log that captures unauthorized changes

Anti-Patterns

• Letting agents self-modify their own rules
• Multiple sources of truth for the same data
• Silent failures (agent encounters error but does not report it)
• Manual fixes without adding automated detection
• Hardcoding values that should come from config

Resources

• Nervous System MCP on npm
• Agent Skills Specification
• Model Context Protocol

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: levelsofself
• Source: levelsofself/mcp-nervous-system
• License: MIT
• Homepage: https://api.100levelup.com/mcp-pricing

Install and usage instructions live in the source repository linked above.