Define the process and the actions the agent may perform.
AI GOVERNANCE · CONTROL · RISK
AI governance for business: define what AI may do, who is accountable and how it is controlled.
AI governance is the system of responsibilities, permissions, policies, controls and evidence that determines how AI may operate inside a business. For an AI agent or AI Employee, it means defining which data it may access, which actions it may propose or execute, who approves sensitive decisions, which risks must stop the workflow and how each action can be reconstructed afterwards. Governance does not replace human oversight; it turns autonomy into bounded, measurable and auditable capabilities.
Decision-oriented reading
12 sectionsStart with the process, review controls, then move to metrics, related routes and sources.
Connect this topicOperational guide →
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1. Governance starts with concrete actions
The first question should not be which model the agent uses, but which actions it may perform inside the process. Reading an order, creating a draft, updating a status or issuing an invoice have different impact levels. Each capability should be described as a concrete operation rather than a generic permission to act across an entire system.
This definition makes it possible to build an autonomy matrix. Some operations can be read-only, others may produce proposals and others may execute automatically under tightly defined conditions. Irreversible, financial, contractual or customer-impacting actions should include stronger controls or human approval.
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2. Separate reading, proposal, approval and execution
A governable architecture clearly separates stages of work. The agent can query a source, interpret a case and prepare a proposal without automatically receiving permission to modify the system. This separation reduces the blast radius and allows quality to be tested before autonomy expands.
Human approval should not be a symbolic screen either. It must show enough context for the person to understand what is proposed, which data were used, which rules passed and what effect the action will have. When approval is recorded alongside the outcome, the organisation can audit decisions and improve policy using real evidence.
DOES IT FIT YOUR PROCESS?
Move from the idea to a diagnosis of your current operation.
Identify what to automate first, which data the workflow needs and where human control must remain.
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3. Define owners for process, system and policy
An AI Employee crosses multiple responsibilities. It may use a CRM administered by IT, execute a process owned by operations and apply a policy approved by management. If something fails, the organisation needs to know who can modify each layer and who makes the final decision.
At minimum, assign a process owner, a technical owner and a policy or risk owner where relevant. This prevents incidents from falling between departments and avoids automation changes being deployed without reviewing their impact on business, data and controls.
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4. Apply least privilege per tool
The agent should not receive broadly privileged credentials for convenience. Each tool should expose only the operations required by the use case. If a workflow reads stock, it does not need permission to change prices, delete products or export the entire database.
Technical identity should be revocable, rotatable and auditable. Permissions should also be separated across environments and processes. A customer-service agent and an invoicing agent may share infrastructure, but they should not automatically share the same scope over ERP or financial data.
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5. Turn policies into executable rules
A policy such as 'do not approve large discounts without authorisation' should be translated into conditions the system can apply: threshold, approving role, required data and behaviour when information is missing. Broad statements express intent, but automation needs concrete rules.
Critical controls should be deterministic whenever possible. The model may interpret intent or extract context, but financial thresholds, valid statuses, document requirements and approval conditions should live in a controllable and verifiable layer.
VALIDATE THE IMPACT
Before expanding automation, calculate whether volume and time justify a pilot.
Compare current manual work with an automated scenario and use that baseline to decide the next step.
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6. Define prohibited actions explicitly
Governance also means declaring what the system may not do. It may be prohibited from changing bank details, deleting records, granting access, exporting full databases or sending confidential documentation without specific approval. An explicit list is safer than expecting the agent to infer the boundaries.
Prohibited actions should be enforced in the tool and permission layer, not only through textual instructions. If an operation should never execute automatically, the most robust design is for the tool not to be available at all or to require external authorisation.
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7. Record decisions and sources without overexposing data
An organisation should be able to reconstruct what happened in an important case: what started the process, which sources were queried, which rules were applied, what proposal the agent produced, who approved it and which action ultimately executed.
This does not require copying all sensitive content into logs. It is better to record identifiers, references, versions, decisions and outcomes. Traceability should be sufficient for investigation while remaining compatible with data minimisation and retention policies.
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8. Create an exception and escalation system
A governed agent should know when to stop acting. Missing data, contradictions, low confidence, out-of-policy requests or integration failures are reasons to halt the workflow and escalate. The ability not to execute is as important as the ability to automate.
The exception should reach a person with useful context. Showing only 'AI failure' forces investigation from the beginning. A well-designed escalation includes state, sources, failed rules, proposed action and the next decision required.
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9. Version prompts, policies, tools and models
System behaviour can change when a prompt, rule, connector or underlying model changes. Without version control, it becomes difficult to know which configuration produced a specific outcome or to return to a stable version.
Every meaningful change should pass through tests and leave evidence of who approved it. Gradual releases allow behaviour to be compared before traffic expands. If metrics deteriorate, rollback should be a known operation rather than an improvisation.
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10. Evaluate quality by process and risk level
A global accuracy average can hide problems. An agent may perform extremely well on simple requests and less reliably on financial or contractual exceptions. Metrics should be segmented by intent, action, criticality and process stage.
Criteria for increasing autonomy should vary as well. Informational classification may tolerate some human correction, while financially impactful actions require stricter thresholds. Governance turns those differences into explicit conditions.
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11. Review access and policies periodically
Permissions that are valid today may no longer be necessary when a process changes or a person leaves the team. Periodic reviews identify excessive access, unused tools and policies that no longer reflect real operations.
Recurring exceptions and actions that are always approved should also be reviewed. Some may become low-risk automation; others reveal an incomplete rule. Governance is not static: operational data should be used to refine controls.
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12. Increase autonomy only when evidence exists
The objective is not to reach full autonomy. The objective is to automate as much as is valuable and safe. A process may remain permanently in proposal mode and still save substantial time by preparing context, validating data and reducing manual work.
When metrics show low correction rates, correctly detected exceptions and stable outcomes, specific capabilities can expand. Growing autonomy operation by operation preserves control and avoids one deployment decision granting excessive authority across the entire system.
DESIGN A PILOT
Turn these rules into a controlled pilot with your data, systems and owners.
We can define scope, integrations, human approvals and the metrics that will show whether automation should expand.
WORKFLOW
Governance framework for an AI Employee
Classify each action as read, proposal, execution or mandatory approval.
Assign process, system and policy owners.
Apply least privilege and remove unnecessary operations.
Turn critical policies into deterministic rules.
Declare prohibited actions and escalation conditions.
Version models, prompts, tools and rules.
Record sources, decisions, approvals and outcomes.
Measure quality by action type and risk level.
Review access, exceptions and autonomy periodically.
METRICS
What to measure
Automatic actions by risk level
Proposals approved without changes
Human corrections
Correctly escalated exceptions
Blocked action attempts
Unused permissions
Approval time
Incidents after version changes
RELATED GUIDE
AI governance framework: how to implement practical controls before increasing autonomy
A practical AI governance framework for turning principles into permissions, ownership, rules, approvals, records, tests and metrics for production AI agents.
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FAQ
Frequently asked questions
What is enterprise AI governance?
It is the set of rules, permissions, owners, controls, metrics and records that determine how an AI system may operate inside a company and how its decisions and actions are supervised.
Does governance reduce automation?
Not necessarily. Good governance can enable more automation because it defines clear boundaries and conditions. Without controls, companies often keep processes manual because of risk concerns.
Which actions should require human approval?
It depends on risk, but financial, contractual, irreversible, access-related, sensitive-data changes or decisions with significant customer impact generally require stronger controls.
Are model instructions enough?
No. Important boundaries should be reinforced through permissions, tools, deterministic rules and external approvals. Text instructions are one layer, not the only control.
How is an AI Employee audited?
By recording sources, versions, decisions, tools used, approvals and outcomes in enough detail to reconstruct relevant cases without storing unnecessary sensitive data.
When can autonomy increase?
When metrics show stable outcomes, low correction rates, correctly detected exceptions and a risk level compatible with the specific action. Autonomy should expand operation by operation rather than globally.
NEXT STEP
Turn this approach into a concrete proposal for your business.
We review the process, required integrations and the scope of a first pilot with you before proposing a broader implementation.