Confidently Wrong: Building Validation and Trust for Your AI Agents

Confidently wrong AI has a price tag and in 2026, it's a line item. Your AI agent will never tell you it's wrong. It doesn't know it's wrong. It ships code with total confidence, answers customers with total confidence and is exactly as confident when it's right as when it just invented a refund policy that doesn't exist, or reported success over a database it quietly broke.

That's the trap of the agent era: confidence isn't correctness, and an agent can't grade its own work. So the failures don't look like failures. They look like green checkmarks and fluent answers — right up until a real user, or a regulator, finds the one that was wrong. It's already expensive: 99% of organizations deploying AI report financial losses from it, averaging $4.4 million each.

The fix isn't a better model, it's an independent layer that verifies what the agent did before it reaches a customer, not after the damage is done. This talk shows what that looks like for both kinds of agents: the ones that build, and the ones that interact. You'll walk out able to tell an agent that sounds trustworthy from one you can actually trust, before your next deployment writes a check your business can't cash.