Overview
Scripted automation follows rules. AI agents make decisions. That distinction between deterministic processes and goal-directed systems that perceive, assess, and act with degrees of autonomy is where conventional governance frameworks reach their limits.
Organisations deploying agentic AI in production are exposing themselves to a category of risk that oversight models designed for traditional automation were never built to address: autonomous actions that are difficult to audit, errors that cascade before human review is possible, and accountability gaps that become liabilities when regulators and auditors ask what an agent did and why.
This webinar sets out a practical governance model for AI agents — one that defines autonomy levels, embeds guardrails into the architecture, and builds accountability into the system rather than the documentation.
What You Will Learn
- What distinguishes AI agents from conventional automation, and why that distinction matters for governance
- The specific risk landscape introduced by autonomous and semi-autonomous agents in production
- A structured governance model covering autonomy levels, operating boundaries, human oversight, and accountability
- How to embed governance into the architecture through policy-as-code, audit logging, and escalation design
- How established service automation and ITSM governance frameworks extend to agentic AI
Who Should Attend
This webinar is designed for CIOs, CTOs, heads of automation, risk and compliance leaders, enterprise architects, and service management leaders who are evaluating or have already begun deploying agentic AI in their operations.
