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.

Choose this repo when

Use this repository when you need the oversight and accountability model for multi-agent systems:

who owns the system
how trust tiers are defined
where human review is mandatory
how failure accountability and audit trails are structured
what monitoring is required at the system level

Do not start here if you need control-flow design patterns. Use agent-orchestration.

Do not start here if you need evaluation metrics and benchmark scenarios. Use agent-eval.

Do not start here if you want runnable examples. Use agent-simulator.

Why multi-agent systems need dedicated governance

Single-agent AI systems are complex. Multi-agent systems compound that complexity:

Emergent behavior: agents interacting can produce outcomes no single agent was designed for
Diffuse accountability: multiple agents can contribute to the same decision path
Trust propagation: a compromised or unreliable agent can influence downstream behavior
Cascading failures: a local failure can propagate through the pipeline
Opaque reasoning: multi-step agent-to-agent flows are harder to audit than single-model decisions
Goal misalignment: local 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
Autonomy Level	Supervised / Semi-autonomous / Fully autonomous
Risk Class	High / Medium / Low
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 such as prompt injection and agent hijacking
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: accountable for overall system behavior
Agent Ownership Map: named owner for each agent
Decision Attribution: how outputs are traced to contributing agents
Failure Responsibility: who is notified and who owns remediation
Audit Trail Requirements: what is logged, where, and 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 decisions
MP.3: risk categorization for emergent multi-agent behavior
MS.2: ongoing monitoring of per-agent and system-level metrics
MG.4: rollback and recovery procedures for agents and full systems

Repository	What it adds
`agent-orchestration`	routing, delegation, validation, and failure-handling patterns
`agent-eval`	evaluation dimensions, scenarios, and reporting structure
`agent-simulator`	runnable behavior and bounded workflow examples
`governance-playbook`	broader enterprise governance operating model
`nist-rmf-guide`	practitioner guide for RMF implementation

Maintained by Sima Bagheri · Connect on LinkedIn

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