AI Governance Is Not Enough Once Agents Start Acting
Most AI governance describes what a system is supposed to do. Enterprise agents force us to care about what it is doing now.
That sounds like a small difference. It is the difference between reviewing a policy and operating a live system.
As Sharjeel Ahmad argues in the context of banking agents, production is where the demo meets the control framework. The controls need to produce evidence while the work happens, not merely describe what should have happened.
A model can change. A prompt can be edited. A new tool or MCP server can be connected. An agent can begin collaborating with another agent that did not exist when the original risk review was completed. The approved diagram remains the same while the running system has quietly become something else.
This drift is how the agentic estate grows. I’ve written about why building agents is the easy part, and operating them is the enterprise problem.
Periodic governance was built for slower systems
Traditional governance assumes assets can be inventoried, assessed and approved at sensible intervals. That works reasonably well when a model is deployed for a narrow prediction and changes through a managed release process.
Agentic systems act across tools and workflows. Their risk depends on the combination: model, context, permissions, collaborators, business purpose and the current state of the task. Assessing any one component in isolation misses the behaviour that matters.
IBM now uses “continuous AI assurance” for the shift from periodic review to ongoing visibility, enforceable controls and accountable ownership. The language is helpful because assurance implies evidence, not merely intention.
Governance tells you the rule. Assurance shows that it held.
A governance policy may say that a product recommendation requires human approval. Assurance should show that the workflow actually stopped, presented the relevant evidence, recorded the decision and prevented the action from being taken through another path.
A policy may restrict access to sensitive customer feedback. Assurance should show which context was retrieved, which model received it and which tools were available during that step.
This requires traces, evaluations and runtime state. It cannot be reconstructed reliably from the final answer alone.
Governance has to travel through orchestration
IBM has also used the phrase “orchestration-led governance”. That gets closer to the architectural point.
Rules become real when the orchestration layer applies them while assembling context, exposing tools, validating outputs and authorising actions. A governance dashboard beside the workflow can report a violation after the event. An orchestration layer can prevent the invalid action or route it to a person before it occurs.
That does not make the orchestration layer the whole governance system. Risk owners still define policy, compliance teams still need oversight, and an enterprise control plane still needs visibility across the wider agentic estate. But the orchestration layer is where policy meets execution.
New to the orchestration layer argument? Check out the full case for why models and agents alone can’t carry enterprise AI.
Evals are part of assurance, not just model testing
Enterprise evals should ask whether the workflow respected the organisation’s definition of good work. Did it use authoritative evidence? Did it stay within its permissions? Did it complete the intended task? Did it stop when confidence or authority ran out?
Traces turn a failed outcome into something that can be examined. Evals turn that failure into a test. The next release can then demonstrate that the workflow improved rather than merely producing a more convincing answer in a hand-picked example.
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Conductor already treats control as part of the workflow
In ProdPad, Conductor keeps workflow state outside the model, limits tools by step, validates actions and can reconcile what actually changed in the product. Human involvement is not an emergency brake added at the end; it is one of the resources the workflow can deliberately invoke.
That gives us the beginnings of continuous assurance at the level that matters: not “the model was approved”, but “this piece of work followed the rules and reached a valid outcome”.
AI governance is necessary. Once agents start acting, it is no longer enough.
Governance records the promise. Assurance needs evidence from the running system.
Explore how Conductor keeps state, validation, permissions and human approval inside the workflow.