Multi-Agent Governance Framework

A practitioner framework for governing systems in which multiple agents, tools, memories, and human principals exchange information or authority.

The repository focuses on the questions that simple “human in the loop” or “trusted agent” labels leave unanswered: what can each component actually do, whose authority is it exercising, how does one agent’s output propagate into another agent’s action, and how can the workflow be contained when evidence or control fails?

Start here

Artifact	Use it for
docs/authority-and-containment.md	describing authority envelopes, propagation controls, containment, and recovery
docs/nist-rmf-mapping.md	practitioner cross-reference to selected NIST AI RMF concepts
agent-orchestration	control-flow patterns
agent-eval	evaluation evidence and decision semantics
agent-simulator	runnable bounded workflow examples

Why a dedicated model is needed

Multi-agent systems can create risk through composition even when each component looks acceptable in isolation.

• Authority amplification: a low-impact recommendation can trigger a high-impact tool through another agent.
• Instruction propagation: retrieved or generated content may be interpreted as executable direction downstream.
• Common-mode error: planner, executor, and validator may share the same model, prompt assumptions, retrieval errors, or policy gaps.
• Diffuse attribution: a final action may combine decisions from several agents, tools, memories, and human approvals.
• State and persistence: one component can alter memory, configuration, credentials, or future context.
• Partial execution: retries and delegation can leave external systems in an uncertain state.
• Containment complexity: stopping the orchestrator may not cancel delegated, queued, or already authorized work.

Governance must therefore cover the interaction graph, not only the individual agents.

Replace “trust tier” with an authority description

A global label such as trusted, semi-trusted, or untrusted is rarely precise enough. Describe each component across these dimensions:

Dimension	Example distinctions
Principal	named user, service account, organization, unknown source
Role	planner, router, executor, reviewer, monitor, memory service
Data reach	public, tenant-scoped, sensitive fields, cross-tenant, no data
Action authority	read, draft, submit, approve, publish, delete, purchase, execute
Externality	private/reversible, internally visible, externally visible, financial, physical
Delegation	none, allow-listed, recursive, capable of granting new authority
Persistence	stateless, session memory, durable memory, configuration or credential write
Validation	executable checks, independent review, human authorization, none
Isolation	sandbox, network boundary, tenant boundary, direct production access
Revocation and recovery	immediate, delayed, partial, irreversible, unknown

Authority should be enforced by identity, data, and tool controls rather than relying on a prompt to remember its limits.

Authority envelopes

An authority envelope defines the actions an agent may take under stated conditions.

For every authority-bearing agent, record:

• principal and identity binding;
• allowed data, tools, and action types;
• prohibited actions and data classes;
• argument and destination restrictions;
• confirmation and dual-control requirements;
• rate, cost, time, and delegation limits;
• logging and provenance expectations;
• revocation, cancellation, and recovery mechanisms.

See the worked structure in docs/authority-and-containment.md.

Governance the transitions, not only the roles

A controlled workflow separates several transitions:

request
  ↓
propose ──► validate ──► authorize ──► execute ──► verify ──► record
             │              │              │
             └─ reject      └─ defer       └─ contain / recover

For each transition, define:

• input schema and provenance;
• accountable owner;
• control or reviewer;
• allowed state change;
• evidence required to continue;
• failure and disagreement behavior;
• timeout and cancellation behavior;
• output record.

A “validator agent” is not automatically independent. Independence can be weakened by common models, prompts, retrieval, context, rubrics, and incentives.

Propagation controls

When one agent consumes another agent’s output, specify:

• whether the output is data, recommendation, or instruction;
• which fields are untrusted content;
• schema and provenance validation;
• authority that may be exercised based on the output;
• conditions that stop propagation;
• how disagreement and missing evidence are retained;
• whether downstream actions can be traced back to the source run.

No agent should gain authority merely because another agent emitted authoritative-sounding text.

Human oversight

“Human in the loop” is not a complete control description. Record:

• the decision the human is authorized to make;
• evidence visible at the review point;
• time available for review;
• whether the interface makes refusal or correction practical;
• consequences of approval, rejection, delay, or no response;
• whether automation bias is measured;
• who reviews the quality of human decisions;
• what happens when the human and system disagree.

For high-consequence actions, separate proposal, authorization, and execution so an approval can be independently attributed and revoked where possible.

Containment and recovery

Every authority-bearing workflow should have a tested response for:

• stopping new work;
• revoking credentials and sessions;
• cancelling queued and delegated actions;
• isolating memory and generated artifacts;
• moving to read-only or draft-only operation;
• identifying completed, partial, and uncertain external actions;
• reversing or compensating for state changes;
• restoring a known-good configuration;
• verifying downstream recovery;
• preserving evidence under privacy and retention controls.

A rollback plan that only restarts the orchestrator is insufficient when delegated work or external actions may continue.

Monitoring

Do not use fixed percentages as universal alert thresholds. Derive thresholds from the workflow’s authority, service objectives, baseline, and harm model.

Monitor:

Level	Signals
Agent	invalid outputs, tool errors, retries, refusal and escalation reasons
Interaction	delegation depth, authority transitions, disagreement, propagation failure
Workflow	completion, partial execution, recovery, human intervention
Control	denied calls, bypass attempts, confirmation failures, revocation health
Outcome	user correction, unauthorized action, incident, harmful external effect

Interpretation matters. A rising escalation rate can indicate safer caution, degraded capability, workload shift, or a broken dependency.

Review gates

Gate	Evidence expected
Purpose and authority review	intended use, prohibited use, authority envelopes, principal model
Component evaluation	task and failure evidence for each material component
Interaction evaluation	propagation, disagreement, common-mode, and cascading-failure scenarios
Control verification	identity, permission, confirmation, revocation, and containment tests
Operational readiness	monitoring, incident ownership, recovery exercise, change process
Decision review	residual risks, conditions, owners, and re-evaluation triggers

The gate outcome should distinguish blockers, evidence gaps, required actions, conditions, and accepted residual risks rather than returning a vague pass/fail label.

Governance record

Maintain a versioned record of:

• purpose and prohibited uses;
• system and delegation graph;
• authority envelopes;
• data and tool boundaries;
• model, prompt, memory, tool, and policy versions;
• validation and authorization points;
• containment and recovery evidence;
• evaluation scope and known gaps;
• owners and incident responsibilities;
• residual-risk decisions;
• changes that invalidate prior evidence.

Maturity and scope

This is a practitioner governance framework, not a formal assurance case, certified control system, or proof that emergent behavior has been fully characterized. Adapt it to the actual authority, externality, jurisdiction, and operational environment.

References to NIST AI RMF are practitioner mappings and should be verified against official material.

Related repositories

Repository	Distinct role
agent-orchestration	routing, delegation, validation, and failure-handling patterns
agent-eval	defensible evaluation design and release-decision semantics
agent-simulator	runnable bounded retries, fallback, and escalation
mcp-agent-risk-checklist	tool-server and agent-integration risk review

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Maintained by Sima Bagheri.