Adam Leon Smith

• Determine where accountability sits when an AI agent takes an action the organisation did not explicitly instruct, predict or approve and what that means for enterprise deployment
• Map the regulatory triggers created by agentic workflows, from the EU AI Act and GDPR to cybersecurity, data and product liability requirements, rather than treating "AI compliance" as a single framework.
• Identify where autonomy, tool use and behavioural drift create new compliance exposure, particularly when organisations can no longer easily trace why an agent acted in a certain way.
• Translate emerging EU AI Act standards into practical guardrails for agentic AI, understanding what needs to be governed, documented and controlled as agents move from recommending actions to executing them.

As organisations scale AI, they face an increasingly complex regulatory landscape shaped by the EU AI Act, the Data Act, the Data Governance Act, and GDPR. Together, these frameworks are redefining how AI systems are built, how data can be used, and who ultimately holds control. The stakes are rising quickly. Under the EU AI Act, non-compliance with prohibited practices could result in fines of up to €35M or 7% of global annual turnover, making AI governance not just a legal concern, but a critical business risk.