Context Beats Chance: Making AI Agents Understand Product and Business Logic in Industry

AI agents often work well in demos but struggle when deployed across large and fragmented corporate data environments. Retrieving the right information alone is not enough: to deliver reliable results in areas such as sales and service, agents must also understand a company’s business rules, product dependencies, and configuration logic.

This session explores how contextual knowledge enables agents to derive and apply this logic and how an agent harness can guide their behavior for a specific use case. Using a real-world case study from a global industrial company, we demonstrate how agents help sales teams identify and configure the right solutions from a complex product portfolio.

Beyond the technical architecture, I will share practical lessons from building, deploying, and scaling such a system across a global organization, from integrating distributed data sources to ensuring reliable outcomes and driving user adoption.