When your AI Agent can't show its work, you stop trusting it with real work
About this session
Trust is the bottleneck in AI agent adoption, not its capability anymore. When an agent can write production code, send emails, and make irreversible decisions on your behalf, "just check the output" is not a viable review strategy. Yet most teams treat explainability as an afterthought: a log dump, a reasoning panel, or a "show your work" toggle bolted on after the real product ships.
This session introduces a practitioner's framework for building explainability that earns trust rather than just performing it. It's built from hands-on experience shipping AI code review at scale. You'll learn to identify the three conditions that make explainability non-negotiable. You'll also discover how to map your user's needs to the three distinct jobs they need to do — verification, debugging, and auditing, each requiring a fundamentally different product response. Finally, you'll walk away equipped to apply a 3-step framework (Audience → Layer → Workflow Moment) to design explainability products at scale.
Speaker
Key takeaways
- The three conditions that make explainability non-negotiable — and how to recognize when your agent has crossed into territory where a trust deficit forms silently.
- The three distinct jobs your users need explainability to do, why one interface can't serve all three, and what the right product response looks like for each.
- A 3-step design framework — Audience → Layer → Workflow Moment — for calibrating exactly the right signal, for the right person, at the right moment.