An AI agent does more than produce text. It may plan steps, call tools, read data, retain memory, and change systems. Governance therefore needs to address authority: what the agent may do, under which conditions, on whose behalf, and with what evidence.
Define an authority envelope
For each agent, document the permitted objective, users, tools, data, actions, transaction limits, environments, and prohibited behavior. Treat this envelope as a control boundary. An agent that drafts an email and an agent that sends one should not share the same approval path.
Assign four owners
Business owner
Accountable for the outcome, intended use, process change, and residual risk.
Technical owner
Responsible for architecture, identity, permissions, testing, releases, and reliability.
Control owner
Responsible for privacy, security, legal, compliance, or other required controls.
Operational owner
Responsible for daily monitoring, exceptions, user support, and incident escalation.
One person may hold multiple roles in a small organization, but the responsibilities must remain explicit.
Gate actions by consequence
Group actions into levels. Read-only retrieval may be allowed within approved sources. Drafting can proceed with review. External communication, record changes, financial actions, access changes, code deployment, and irreversible steps should require stronger conditions or human approval.
Approval should be meaningful. The reviewer needs the proposed action, supporting evidence, uncertainty, affected record, and a clear way to reject or modify it.
Govern the full execution path
Assess prompts and models, but also tools, connectors, memory, identity, retrieved content, downstream systems, and fallback behavior. Indirect prompt injection can arrive through a document or webpage. Memory can preserve a bad instruction. A legitimate tool with excessive permissions can magnify a small error.
Require production evidence
Before launch, test normal tasks, ambiguous requests, malicious inputs, unavailable tools, conflicting instructions, permission failures, and recovery. Record acceptance thresholds and negative results. Limit the initial scope and make rollback possible.
Monitor decisions and exceptions
Log the agent version, relevant inputs, tool calls, approvals, outputs, failures, and overrides while respecting data-minimization requirements. Review near misses and repeated human corrections. These are signals that the authority envelope, instructions, or workflow may be wrong.
Reapprove material changes
A new model, tool, permission, memory design, data source, user group, or action type can change risk. Define which changes require testing and approval rather than assuming the original decision still applies.
AI agent governance works when autonomy is deliberate. The question is not whether an agent is intelligent enough to act. It is whether the organization has authorized that action and can show that the surrounding system remains controlled.