Our expertise

AI agent governance and oversight

AI does its part. You keep the final say. Define access, approvals and responsibilities for AI agents. Build in activity records, result monitoring and a clear path for human handover.

Your tools. Your rules. Your team in control.
Interactive example

You set the boundaries

Set the permissions, then test an action.

Prepare a draftUsing approved sources
Request permission to sendHuman approval required
The guardrail in action

Your rules are ready.

Test an action to see which rule applies.

Illustrative simulation. No real actions.

01

An action scope teams understand

02

Operations and results available for review

03

Clear responsibilities and handover paths

AI agent governance: define access, actions and oversight

A useful assistant needs clear limits. Before delegating a task, your teams should know which information it can read, which actions it can prepare or perform, and when it must stop. We turn those decisions into rules for both the software and everyday working practices.

The framework considers the task, its consequences and the people involved. It can evolve with the scope, provided changes to access and responsibility remain visible and are reviewed with the appropriate people. Everyone should be able to understand what has been delegated and where a human decision still belongs.

Give each action an appropriate level of control

We distinguish permitted tasks, results that need review and actions that require approval. Incomplete or unusual cases have a handover path. Responsibilities are defined so an alert has a clear owner and the next step can be understood.

Control point Role in operation
Access Limit information and tools to the agreed need
Approval Involve a person before selected actions
History Review recorded operations, results and errors
Alert Notify the person responsible for an event
Pause and recovery Stop the sequence and resume with the necessary context

The information retained in the history follows your monitoring needs and the rules applicable within your organisation.

Test the paths that do not go as expected

A response may be incomplete, a tool unavailable or a request outside the agreed scope. We design tests to observe those situations and check the intended response: request clarification, avoid acting, flag an error or pass the case to someone.

Oversight needs to be workable for the team responsible for it. Alerts, instructions and handover information are therefore reviewed with the people who will use them, alongside the result the agent produces. This helps reveal controls that look useful on paper but are difficult to apply in practice.

Use the review to improve what follows

Quality, checking time, errors and human handovers provide reference points for adjustments. A review may lead to a source correction, reduced permission or a new test. It may also confirm that some work should remain with people.

This framework supports building your assistants and helping teams adopt them. Autonomy has a place within an organisation that retains the ability to understand, decide and intervene.

Good questions.
Clear answers.

Why plan oversight from the beginning?

Decisions about access, approvals and recovery shape the agent’s design. Planning them early allows them to be tested alongside the rest of its operation.

Does the history explain everything the model thinks?

No. It can record the relevant inputs, actions, results, approvals and errors included in the scope. It does not guarantee a complete explanation of the model’s internal reasoning.

Can you assess an assistant already in use?

We can review how it operates, its available access and existing records. The improvement plan then depends on what the solution and its environment support.

Something in mind?
Let’s give it momentum.

Tell us what slows you down. We’ll explore the next step together.

Let’s talk about your projectA first conversation. A project that fits.