AI agent governance: how to keep autonomous AI accountable

AI agents plan multi-step tasks and carry them out by calling tools: reading and writing files, querying databases, sending emails, running code or updating business records. Coding agents work inside developer environments, support agents work inside CRMs, and operations agents connect to production systems.

That autonomy is the point, and also the risk. A single wrong output can now become a wrong action, at machine speed, using whatever permissions the agent holds.

• Excessive agency. The OWASP Top 10 for LLM Applications lists excessive agency, meaning more functionality, permissions or autonomy than a task needs, as a top risk.
• Human oversight. The EU AI Act requires high-risk AI systems to be designed so people can effectively oversee them (Article 14).
• Accountability. When an agent acts, someone must own the outcome, and auditors will ask who approved what.
• Shadow agents. Agents are easy to connect and easy to forget, which makes them a fast-growing form of shadow AI.

Coding agent

A coding agent may read the repository and run tests freely, but pushing to the main branch or sending code to an external service requires approval, and secret patterns in its context are blocked.

Customer support agent

A support agent can look up orders and draft replies. Refunds above a threshold and emails to external domains are held for a person to approve.

Lending agent

An agent preparing credit decisions can gather applicant information, but any decision above a set amount is routed to a credit officer and a risk owner, and the decision record is kept as evidence.

• Destructive or irreversible actions taken without review.
• Prompt injection leading an agent to misuse its tools.
• Over-privileged credentials shared across agents.
• Sensitive data sent to external services.
• No record of who or what made a decision.

• NIST AI RMF: Govern, Map, Measure and Manage apply to agentic systems as to any AI system; the Generative AI Profile (NIST AI 600-1) adds guidance for generative AI risks.
• ISO/IEC 42001: agents fall within the AI management system, including roles and responsibilities, risk treatment and operational monitoring.
• EU AI Act: where an agent is part of a high-risk AI system, requirements such as risk management, record-keeping and human oversight apply.
• OWASP Top 10 for LLM Applications: recommends limiting agent functionality and permissions, enforcing authorization in downstream systems, and requiring human approval for high-impact actions.

1. Inventory agents before writing agent policy; you can't govern unknown agents.
2. Give each agent its own identity and credentials; avoid shared keys.
3. Start with approval gates on a short list of high-impact actions, then refine.
4. Keep enforcement deterministic and outside the model.
5. Log tool calls with enough context to reconstruct a decision later.
6. Have a way to pause an agent quickly, and test it.
7. Review overrides and incidents regularly and update policy.

ReguLattice treats AI agents as first-class AI systems. It inventories agents with an owner, records what they do at runtime, including tool calls, and checks actions against deterministic policy rules.

High-risk actions can be held and routed to designated approvers, such as a compliance officer and a risk owner. Each decision is linked to its evidence, controls and risks in an audit trail, mapped to ISO/IEC 42001, the NIST AI RMF and the EU AI Act. See how an AI governance platform works.