Governance of AI agents / governance of agent-based AI: Do you see the difference ?

These two areas overlap but are not identical. They address fundamentally different questions.

AI agent governance focuses on entities: what an agent is, who owns it, how it is built, and how it is authorized to operate. This is static governance. It covers the agent’s lifecycle—design, deployment, authorization, auditing, decommissioning—and the associated liability issues (who is liable if the agent causes harm?). It applies equally to a single agent and to a fleet of agents.

Agent-based AI governance focuses on behaviors: what happens when an AI system acts autonomously in an open environment, makes a series of decisions, utilizes tools, and interacts with other systems? This is dynamic governance. It concerns less the agent as an entity than the action loops it produces: autonomous planning, use of tools, delegation to sub-agents, and side effects on the systems it affects.

What this distinction means in practice

AI agent governance falls more within the purview of IT, legal, and compliance functions. It produces registries, deployment policies, and accountability matrices. It naturally builds on ISO 42001 governance or an AI asset management framework. An agent is treated as an asset to be inventoried and monitored.

Agent-based AI governance falls more under the purview of risk architecture and operational security. It focuses on behavioral loops: what the agent actually does when released into an environment. The questions it raises are: what happens if the agent interprets an instruction in an unexpected way? How do we prevent a delegation loop from spiraling out of control? Who can interrupt a chain of actions in progress? It aligns with what we previously described under the concepts of hyperagents and the Agentic Risk Standard.

Why the distinction is useful

Without it, we tend to produce governance frameworks that manage agent catalogs well but remain silent on emergent behaviors, particularly in multi-agent architectures where it is no longer a specific agent that poses a problem, but the dynamics of their interaction. A well-governed agent (clear identity, documented rights) can certainly produce poorly governed agentic behaviors (unsupervised action chains, use of APIs with irreversible effects, opaque delegation to sub-agents).

This distinction also helps define responsibilities: governance of AI agents is often the responsibility of the IT department or the Chief Information Security Officer (CISO), whereas governance of agent-based AI requires shared responsibility with the business units that define the agents’ objectives and acceptable thresholds of autonomy.

Agent-based AI Transformation in Businesses

Agent-based AI transformation refers to a company’s transition from one that uses AI (to assist, generate, or recommend) to one in which a portion of its operational processes are handled autonomously by AI systems—agents that perceive context, plan, act, delegate, and adapt without human intervention at every stage.

This is not an evolution of digital transformation. It is a fundamental shift: we are no longer digitizing human processes; we are partially transferring them to systems capable of initiative.

The scope of this transformation spans five layers, the first two of which (data, AI capabilities) still fall under a traditional digital transformation agenda, while the next three (agents, orchestration, reconfiguration of work) constitute the agentic core proper.

What this approach implies for driving the transformation

Three practical implications emerge.

1. The first concerns the sequence. We cannot manage the governance of agent-based AI until we have laid the groundwork for the governance of AI agents. An agent that is not inventoried and lacks clear ownership cannot be subject to a supervision policy. The order is not merely theoretical; it is an operational dependency.

   
   

2. The second concerns organizational scope. The governance of AI agents remains largely the responsibility of the IT department and compliance—it is a matter of asset management and accountability. Agent-based AI governance, on the other hand, extends all the way up to stratum 5: it involves the reconfiguration of business functions, the levels of autonomy that functional departments are willing to delegate to agents, and escalation processes. It cannot be managed without the business functions taking the lead.

   
   

3. The third is specific to stratum 3, which is the junction point. This is where the most delicate trade-offs are made: what level of operational autonomy for a given agent? Who validates? Who can interrupt? These questions require a joint decision between the CIO, business units, and compliance.

   
   

Layer 3 is the natural point of tension in any agent-driven transformation, and this is where our Meridian approach, in its “Facilitate” dimension, offers the greatest value.