Building AI Agents That Actually Work in Production

AI agents are moving quickly from experimental demos to real production applications. But building an agent that works once is very different from building a system that can reliably operate when users, tools, APIs, memory, latency, and unexpected failures enter the picture.

Unchecked tool loops, unvalidated model outputs, and missing state recovery can drain API budgets, corrupt session states, and cause silent production outages.

In this live online session, we'll break down how modern AI teams design production-ready agentic systems. We'll explore how agents make decisions, call tools, manage state, recover from failures, and remain observable when deployed inside real products. This is not a generic introduction to AI agents. We'll focus on the architecture, engineering decisions, and failure patterns that matter when you move from prototype to production.