Agentic AI Governance
A controlled transition from pilot to enterprise operation
Agentic AI systems can plan tasks, use tools and execute actions across multiple steps. This increases their potential value, dependencies and operational risks. Governance therefore needs to address boundaries, monitoring and intervention from the start.
When is agentic AI governance needed?
Moving from pilot to operation
An agentic use case is about to enter production, scale or connect to critical business processes.
Multi-step decisions and actions
The system plans autonomously, accesses data or tools and initiates actions that affect customers, employees or business processes.
Unclear boundaries
It is not yet clear which decisions the system may prepare, execute or take only with human approval.
New control requirements
A conventional model approval is insufficient because behaviour, tool use and process context need ongoing monitoring.
What we design
Boundaries and autonomy levels
Define permitted tasks, data access, tools, actions and decision authority in line with risk and business context.
Human oversight
Define meaningful human checkpoints, approvals and intervention options while retaining the operational value of the system.
Monitoring and traceability
Specify logging, performance and risk measures, anomaly detection and traceable operational decisions.
Escalation and shutdown
Set thresholds, escalation paths, stop criteria and responsibilities for incidents, unexpected behaviour and unintended effects.
Change and supplier management
Control model, prompt, tool and process changes, including dependencies on platforms, providers and external components.
What you receive
A governance and control model for the specific agentic use case or a portfolio of related use cases.
Defined autonomy levels, approval criteria, human oversight checkpoints and escalation paths.
An agreed set of operational controls for development, testing, approval, operation, monitoring and change.
A roadmap for a controlled transition from pilot to production use.
Approach
1. Use case and system boundaries: clarify objectives, process context, stakeholders, data, tools, actions and technical dependencies.
2. Risks and controls: identify material failure modes, impacts and control needs across the agentic workflow.
3. Governance design: agree responsibilities, autonomy boundaries, approvals, monitoring, escalation and change control.
4. Pilot and operational approval: test controls in the actual application context, close critical gaps and prepare a sound decision on production use.
Engagement scope
Typically three to six months. Effort depends on autonomy, process criticality, the system landscape and pilot maturity.
Technical security assessments, legal assessments and specialist model tests are conducted with the relevant internal or external experts where required.
Autonomy needs clear boundaries
Good agentic AI governance enables innovation by clarifying risks, intervention and accountability before production use. Engagements are available from 2 November 2026.