TL;DR: Governing a model means auditing its answers. Governing an agent means controlling its actions, while it acts. The difference is not one of degree: an agent accesses systems and triggers operations without validation at each step. Three building blocks carry most of the load: the inventory of agents in service, rules enforced at execution time and the ability to stop an agent immediately.
88% of organisations use AI in at least one business function (Deloitte, State of AI, 2025) and fewer than one in five has a formal governance framework for its autonomous agents. The gap matters, because an agent does not just answer: it acts on real systems. This guide sets out what AI governance is, what the arrival of autonomous agents changes, the five steps to put it in place and the five pillars of a framework that holds.
AI governance refers to the set of rules, responsibilities and processes that structure the rollout, oversight and responsible use of AI systems in an organisation, throughout their life cycle. It defines who can do what, within what clear framework of responsible governance and according to which criteria of responsible use. For each risk level, the required human oversight is precisely calibrated. That way, every system is steered reliably and transparently. Its scope covers all AI systems: language models, autonomous agents and automation systems.
Did you know? 88% of organisations use AI in at least one business function (Deloitte State of AI 2025). Yet fewer than one in five has a formal governance framework to ensure the responsible use of its AI agents. Good practice for effective governance means clearly defined human intervention for each type of agent.
Traditional artificial intelligence governance oversees a model's outputs: bias, accuracy, compliance of answers. It is passive governance, focused on data and results. For a company using only non-agentic LLMs, this level of oversight can be enough.
Agentic AI governance is of a radically different nature. An autonomous AI agent does not analyse: it acts. It executes workflows, accesses internal systems and makes decisions without direct human oversight. Governing an agent means controlling its actions in real time, not just auditing its outputs.
This requires specific tools: an agent registry to inventory every agent in production, a policy engine to apply rules on the fly and a kill switch to stop any non-compliant behaviour immediately. Agentic control mechanisms operate at a granularity that classic governance tools do not cover.
Regulatory and competitive pressure, combined, makes 2026 a year nobody can sidestep.
On the regulatory side, the European timetable has shifted. The transparency obligations in Article 50 of Regulation (EU) 2024/1689 have applied since 2 August 2026, while compliance for the high-risk systems in Annex III has been postponed to 2 December 2027 by the digital omnibus regulation that entered into force on 27 July 2026. The postponement buys time; it does not change the obligations themselves: they remain on the books.
