From Product Data to Agent Decisions: Building the Trust Layer for Agentic Commerce
About this session
Most organizations approach agentic AI as a model-selection or prompt-engineering challenge. In commerce, the more persistent problem is whether the agent has decision-grade information it can safely act on.
Drawing on enterprise work across product information management, digital experience platforms, intelligent search, and AI-enabled B2B/B2C commerce, this session presents a practical framework for turning fragmented product data into a trustworthy foundation for AI agents. I will show why an agent can sound intelligent while still making poor recommendations, and why the failure usually begins upstream: missing attributes, conflicting sources of truth, unclear policy rules, stale availability, or no mechanism to express confidence and escalate uncertainty.
Attendees will learn a five-part “trust layer” for agentic commerce: establish authoritative data contracts; define decision-critical attributes; constrain actions with policy and permissions; evaluate recommendations against realistic buyer scenarios; and observe failures so the system improves rather than silently repeats mistakes.
The session will also cover the difficult realities teams encounter in production—legacy data inconsistencies, competing business ownership, incomplete taxonomy, and the pressure to launch before data is perfect. Rather than waiting for a flawless foundation, attendees will leave with a practical method to prioritize the data and controls that matter most for the customer decision at hand.
Speaker
Key takeaways
- Identify whether their AI agent has information that is merely searchable or genuinely safe to act on
- Create a decision-critical data checklist for one high-value customer or operational workflow
- Build a lightweight evaluation and escalation process before expanding an AI agent into production