The Obstacles to Deploying AI Agents to Production

Your AI Agent works great in the demo. Then you deploy it to production, and it goes off the rails, hallucinating a refund policy that costs you thousands, triggering race conditions nobody expected, leaking credentials, or getting exploited by someone who burns through your tokens.

The harsh truth is that as AI delivers on tasks that used to take founders months, and it can leave thousands of issues in its wake. The truth is that as AI agents take on customer-facing tasks that used to require human judgment, they can create thousands of issues at the same speed they solve them. But there are patterns and techniques you can use to fix this so you can take your product from a cool startup experiment to something you can build a business on.

In this session, Morgan Willis will walk through what it takes to build AI agents you can trust in production. She'll walk through a layered approach that combines input validation, output filtering, LLM-as-judge steering, and action approval—the layers you need to turn a vibe-coded experiment into actual infrastructure.