multi-agent-governance
Multi-Agent Governance Framework
A governance framework for multi-agent AI systems — covering oversight, accountability, inter-agent trust, and risk management for systems where multiple AI agents collaborate or compete to complete tasks.
Why Multi-Agent Systems Need Dedicated Governance
Single-agent AI systems are complex. Multi-agent systems compound that complexity:
- Emergent behavior — agents interacting produce outcomes no single agent was designed for
- Diffuse accountability — when multiple agents contribute to a decision, who is responsible?
- Trust propagation — an agent that trusts a compromised agent can become an attack vector
- Cascading failures — a single agent’s failure can propagate through the entire pipeline
- Opaque reasoning — chains of agent-to-agent communication are harder to audit than single-model decisions
- Goal misalignment — individual agent objectives can conflict with system-level objectives
Framework Components
1. Agent Classification
Before deploying a multi-agent system, classify each agent:
| Dimension | Categories |
|---|---|
| Role | Orchestrator / Executor / Validator / Monitor |
| Trust Level | Trusted / Semi-trusted / Untrusted (for external agents) |
| Autonomy Level | Supervised / Semi-autonomous / Fully autonomous |
| Risk Class | High / Medium / Low (based on possible impact of failure) |
| Data Access | Read-only / Read-write / No access |
2. Inter-Agent Trust Model
┌─────────────────────────────────────────────────────────┐
│ ORCHESTRATOR │
│ (high trust, human-supervised) │
└──────────────────────┬──────────────────────────────────┘
│ delegates tasks + validates outputs
┌───────────┼───────────┐
▼ ▼ ▼
┌──────────┐ ┌──────────┐ ┌──────────┐
│ AGENT A │ │ AGENT B │ │ AGENT C │
│(executor)│ │(executor)│ │(validator│
│semi-trust│ │semi-trust│ │high-trust│
└──────────┘ └──────────┘ └──────────┘
│ │
▼ ▼
┌──────────┐ ┌──────────┐
│EXTERNAL │ │ HUMAN │
│ TOOL │ │ REVIEW │
│(untrusted│ │(required │
│ sandbox) │ │for high │
└──────────┘ │ risk) │
└──────────┘
3. Governance Gates for Multi-Agent Systems
| Gate | Timing | Requirements |
|---|---|---|
| System Design Review | Before development | Agent roles, trust model, and failure modes documented |
| Individual Agent Validation | Before integration | Each agent validated independently |
| Integration Testing | Before staging | End-to-end scenarios including failure injection |
| Red Team | Before production | Adversarial scenarios: prompt injection, agent hijacking, goal manipulation |
| Human Oversight Check | Before production | Human review points defined for all high-risk decision paths |
| Monitoring Activation | At deployment | Per-agent and system-level monitoring configured |
4. Accountability Framework
For every multi-agent system, document:
- System Owner — the person accountable for the system’s overall behavior
- Agent Ownership Map — named owner for each agent in the system
- Decision Attribution — how to trace a system output to the contributing agents
- Failure Responsibility — who is notified and responsible when which agent fails
- Audit Trail Requirements — what gets logged, at which agent, for how long
5. Monitoring Requirements
| Metric | Frequency | Alerting Threshold |
|---|---|---|
| Task completion rate | Real-time | Drop > 10% from baseline |
| Inter-agent latency | Real-time | P99 > configured SLA |
| Agent error rate | Real-time | Error rate > 1% |
| Goal drift (planned vs. actual) | Per task | Configurable by use case |
| Token usage / cost | Hourly | > 150% of daily budget |
| Human escalation rate | Daily | > 20% increase week-over-week |
NIST AI RMF Alignment
Full mapping: docs/nist-rmf-mapping.md
Key NIST AI RMF categories addressed:
- GV.4 — Human oversight for high-autonomy agent decisions
- MP.3 — Risk categorization specific to multi-agent emergent behavior
- MS.2 — Ongoing monitoring of per-agent and system-level metrics
- MG.4 — Rollback and recovery procedures for individual agents and full systems
Ecosystem
| Repository | Purpose |
|---|---|
| agent-system-simulator | Simulate and evaluate multi-agent system behavior |
| multi-agent-orchestration-patterns | Orchestration design patterns for multi-agent pipelines |
| ai-agent-evaluation-framework | Evaluation metrics and benchmarks for AI agents |
| enterprise-ai-governance-playbook | Organizational governance playbook |
| nist-ai-rmf-implementation-guide | NIST AI RMF practitioner guide |
Maintained by Sima Bagheri · Connect on LinkedIn