Agents you can trust with real work.
Permissions, guardrails, approvals and audit trails that keep every agent inside the rules you set, and your team in charge of the decisions that matter.
See the four layersAI agent governance
Autonomy is only useful when it stays inside clear boundaries.
An agent that can read your CRM, send email and update invoices needs the same care you give a new hire with the same access: a defined role, limited permissions, someone to approve the big decisions, and a record of what it did.
Agent governance is how we build that in. We agree what each agent may do, what it may never do, and when it must stop and ask a person. Then we test those rules, log every run, and review the agent regularly after launch. The result is an agent your operations, finance and security teams can all sign off on.
Four layers of control
Every agent is wrapped in the same four layers.
Choose a layer to see what it does. Each one catches what the layer inside it lets through.
Least privilege
Decide exactly what each agent can touch.
Select a cell to cycle it between blocked, needs approval and allowed. Each agent gets only the access its job requires.
Tested on purpose
Try to push the agent outside its rules.
Before launch we test agents with awkward and hostile requests. Pick one and see which layer deals with it.
Governed for life
Governance does not end at launch.
Rules, tests and reviews continue for as long as the agent runs, so it stays accurate as your data, policies and regulations change.
-
Define
We agree the agent's role, permissions, limits and the actions it must never take, together with a named owner on your side.
-
Test
The agent is run on samples of your real cases, plus deliberate attempts to break its rules, and the results are measured.
-
Pilot
Your team reviews what the agent does before any limits are relaxed. Nothing sensitive happens without a person.
-
Monitor
Every run is logged. Dashboards and alerts show errors, handover rates and drift, so problems are seen early.
-
Review and update
Prompt, model, tool and limit changes follow a controlled process with testing first, and the owner signs off on each one.
Built for accountable action
Put agents to work, with your rules in charge.
Start with one workflow. We define the limits with your team, test them, and show you every step the agent takes.
Questions
Answers before you ask.
What is AI agent governance?
AI agent governance is the set of rules, controls and records that decide what an agent may do, when it must ask a person, and how its work is checked afterwards. It covers permissions, guardrails, approvals, audit trails, testing and ongoing review, so an agent can act inside your systems without acting outside your intent.
What guardrails do you put around an agent?
Typical guardrails include limited tool access, spending and action limits, approved sources for answers, output validation before anything is sent, confidence thresholds, and a list of actions the agent must never take. We agree them with your team before the agent is built and test them before launch.
How do human approvals work?
You choose which actions need a person, such as refunds above a limit, payments to new vendors or messages to key accounts. The agent pauses, sends the approver the full context and its suggested action, and carries on only after a decision. Anything the agent is unsure about goes to a person instead of being guessed.
What is recorded in the audit trail?
Every run records what the agent saw, what it decided, which tools it called, what it changed and who approved it. Records are searchable by agent, customer, record and date, so your team can answer what happened and why.
How do you limit what an agent can access?
Each agent gets only the systems, folders and fields its job needs, with read-only access where writing is not required. Sensitive fields can be masked, and an agent cannot reach records outside its scope, even if a message asks it to.
How do you test an agent before it goes live?
We run the agent on samples of your real cases, including awkward ones and deliberate attempts to push it outside its rules, and measure the results. A pilot follows, in which your team reviews what the agent does before limits are relaxed.
Who is accountable when an agent makes a mistake, and does governance cover compliance?
Accountability stays with your organisation, so each agent has a named owner on your side who sets the rules and reviews exceptions. The audit trail shows exactly what happened so causes can be found and fixed. Governance supports your compliance work but does not replace it, so your legal and security teams should review the setup against your own requirements.
How is an agent governed after launch?
We monitor runs, track errors and handover rates, and review samples with your team on a regular schedule. Changes to prompts, models, tools or limits go through a controlled update with testing first, so an agent does not drift as your data and rules change.