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Agentic AI governance is the control of AI systems that plan and take actions on their own, as opposed to systems that only produce predictions or content. It covers what an agent is permitted to do, the limits on its autonomy, how its actions are logged, and who is accountable when an agent acts wrongly.
How does it differ from governing models?
Governing a predictive or generative model centers on the quality of its output: accuracy, bias, explainability, and whether the content is safe. An agent adds a second surface, which is behavior. It selects its own steps, calls tools, and changes state in systems that matter. The governing questions become what the agent is allowed to touch, what it can do without asking, how far it can go before a person is involved, and what record exists afterwards. A model that produces a wrong answer creates a bad decision; an agent that acts wrongly creates a completed transaction.
Which controls are specific to agents?
Four groups do most of the work. Autonomy classification establishes how much independence a given agent has, from suggesting through to acting with confirmation through to acting alone. Action permissioning defines the specific operations an agent may perform and the systems it may reach, usually narrower than the human role it inherited. Escalation and thresholds set the conditions that force a human decision, such as monetary value, confidence level or an unrecognised situation. Action logging records every tool call and state change so that behavior can be reconstructed later.
Where do the regulatory expectations come from?
There is no single agentic AI statute, and the expectations are assembled from several places. The EU AI Act applies through its risk classification and its transparency and human oversight duties. Singapore's IMDA published a Model AI Governance Framework for Agentic AI in January 2026. In US banking, SR 26-2 takes the opposite approach and puts agentic AI out of scope, which leaves firms to define their own standard and be ready to defend it. On the security side the OWASP Top 10 for Agentic Applications gives a shared risk vocabulary.
What makes multi-agent systems harder?
Once agents call other agents, several assumptions break. Authority becomes transitive, so an agent with narrow permissions can reach further by asking another agent that holds broader ones. Failures propagate, and a wrong output early in a chain is treated as fact by everything downstream. Attribution gets difficult, because reconstructing which agent caused an outcome needs correlated logs across all of them. Governance therefore has to operate on the system, not on each agent in isolation, with identity that carries across calls and a trace that spans the whole chain.
What should be in place before deployment?
A registered entry for the agent in the AI inventory, with a named owner. A written statement of purpose and the actions it is permitted to take. An autonomy level with the escalation conditions that override it. Credentials scoped to the agent itself and not borrowed from a person. Logging sufficient to answer what the agent did and why. A defined off switch and a person responsible for using it. Most incidents involving agents trace back to one of these being absent, not to a failure of the model underneath.
Real world example:
A procurement team runs an agent that reviews supplier invoices, checks them against purchase orders and approves payment. It is classified as acting with confirmation above 2,500 pounds and acting alone below it. Its database credential is its own, scoped to read purchase orders and write payment approvals, with no access to supplier bank details. Any mismatch it cannot resolve escalates to a named approver. Every check and approval is logged with the invoice reference and the rule applied. When a supplier submits an invoice with instruction text embedded in the description field, the agent's separation of retrieved content from instructions stops the goal hijack, and the attempt is captured in the log for the security team.
