AI Agent Governance: Identity, Authority and Accountability

An agent does not become safe because its prompt says to be careful

Consider an agent that can inspect a supplier record, query a knowledge base and update a procurement system. Each tool call may be technically valid while the combined business action is not. The question for a CIO is not simply whether an agent can perform a task. It is who authorized that action, what evidence it used, which constraints applied at the time and who can reverse the outcome.

Five controls to implement before scaling

1. Identity and scoped authority

Every executing agent needs an attributable workload identity. Tool permissions should be bound to task, tenant and business context rather than inherited indiscriminately from an administrator or the requesting employee.

Conceptual reference pattern; enterprise policies and integrations determine implementation.

2. Policy enforcement at the action boundary

Validate the proposed action immediately before execution. High-impact changes—approving contracts, changing payment details, disabling controls—require explicit policy decisions and, where appropriate, a human approval.

3. Traceability beyond model logs

Record the triggering event, agent and tool versions, retrieved evidence, policy decision, human decision, resulting transaction ID and completion status. An LLM transcript alone is not an audit trail of business state.

4. Failure and exception management

Design for timeouts, duplicate requests, partially completed updates, revocation and escalation. Idempotency keys and compensation paths matter more than a persuasive demo.

5. Ongoing inventory and oversight

Maintain an agent registry with owners, declared capabilities, data scope, deployed versions and monitoring. Revoke access when a capability or integration is retired.

Reference architecture: inspect → authorize → act → evidence

StageResponsibilityRequired evidenceRequestAuthenticate principal and identify intentTenant, user, purposePlanGenerate bounded proposed actionsInputs and tool selectionAuthorizeCheck policy and approvalsDecision and policy versionExecuteInvoke tools with scoped credentialsTransaction and idempotency IDsObserveReconcile outcomes and exceptionsBusiness state and audit record

What to measure

Measure unauthorized-action blocks, unowned agents, approval latency, failed compensations, untraceable tool calls and percent of actions reconciled with source systems. These are more useful operating signals than the number of agents deployed.

Archy Platform is designed around coordinated agents and enterprise workflows. The relevant design question is how to place agent execution inside accountable business and technical boundaries—not how to increase autonomy for its own sake.

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Approval belongs to a specific action

A reviewer approves updating a supplier’s postal address. Before execution, another event changes the proposal to include bank-account details. Reusing the earlier approval would be a control failure even if every API call is valid. Bind approval to the action payload, relevant evidence and case revision. Recheck those bindings at execution, and request a new decision when a material field changes.

Keep separation of duties where the business process requires it. The agent that prepares a change should not gain permission to approve its own exception. A human gate is meaningful only when the reviewer can inspect the proposed change, understand its evidence and reject it without the workflow proceeding through another tool path.

Test negative cases, not just completion

Use synthetic records to test cross-tenant access, revoked credentials, stale approvals, duplicate delivery and malicious instructions inside retrieved documents. A passing result includes both a visible denial and enough context for an operator to understand it. A silent failure that leaves the case apparently complete is not an effective control.

Ownership must extend beyond launch. Assign responsibility for permissions, tool contracts, incident handling and retirement. When an integration changes, repeat the relevant control tests. When a capability is withdrawn, check cached credentials, queued tasks and waiting cases. The architecture is accountable when the organisation can explain the agent’s effective authority at the moment an external change occurred.

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For the related architecture decisions, see AI Agent Sandboxing: Enforcing Authority Boundaries; AI Agent Observability with OpenTelemetry; AI Agent Instruction Governance: Scope, Versions and Audit.