Beyond the AI Agent: Building the Trust Layer for Enterprise AI
About this session
AI agents are becoming increasingly capable of reasoning, orchestrating workflows, and making decisions across enterprise systems. But building an intelligent agent is only part of the challenge. The harder question is: what must exist around that agent before an organization can actually trust it?
This session explores the often-overlooked “trust layer” required to move AI from experimentation to responsible enterprise adoption. We will examine how data lineage, business context, governance, exception handling, human oversight, explainability, and feedback mechanisms work together to create AI systems that are not only intelligent, but operationally reliable.
Using practical enterprise scenarios, the session will show how seemingly small gaps - unclear data provenance, missing business rules, poorly designed escalation paths, or insufficient human checkpoints - can become significant risks once AI begins acting autonomously.
Attendees will be introduced to a practical framework for evaluating AI-enabled and agentic workflows across five questions: What does the system know? Where did that information come from? What is it allowed to decide? When should a human intervene? And how does the system learn when something goes wrong?
The goal is to shift the enterprise AI conversation from simply “Can we build it?” to the more important question: “Can we trust it to operate?”
Key Takeaway - Apply a practical five-question framework: Leave with a reusable approach for evaluating whether an AI workflow is ready to move from experimentation toward trusted enterprise use.
Speaker
Key takeaways
- Design the trust layer around AI: Learn how data lineage, governance, controls, explainability, and human oversight fit together around an AI or agentic workflow.
- Build for exceptions, not just the happy path: Understand why enterprise AI architecture must account for ambiguity, unexpected data, failed decisions, and escalation from the beginning.
- Connect technical and business context: Learn why technically correct AI outputs can still produce poor enterprise decisions when business meaning, ownership, or data context is missing.