Most AI agent programs do not stall because the model underperforms. They stall when someone asks who is accountable if the agent gets it wrong, and which control it broke. Nobody can answer. A process map will not answer it. Neither will more context. 

Gartner expects more than 40% of agentic AI projects to be canceled by the end of 2027, citing inadequate risk controls. 

Join iGrafx’s Senior Vice President for North America Sales, Daniel Hughes as he explains why the next wave of AI failures will be accountability failures, and the four layers of an Agentic Process Foundation (APF): Visibility, Prioritization, Context, and Optimization. 

What we will cover:

• The distinction between process intelligence, process context, and a governed answer to who is accountable, and why the first two stop short. 
• How to build a risk register an agent can actually query before it acts, rather than one scattered across spreadsheets, audit binders, and institutional memory. 
• How to simulate an agent’s behavior under its real risk and control constraints before it reaches production, instead of discovering the exception path after go-live. 
• How to catch conformance drift as drift, in time to correct it, rather than as an audit finding after the fact.