Let’s face it: most AI agents are glorified demos. They look flashy, but they’re brittle, hard to debug, and rarely make it into real products. Why? Because wiring an LLM to a few tools is easy. Engineering a robust, testable, and scalable system is hard.

This talk is for practitioners, data scientists, AI engineers, and developers who want to stop tinkering and start shipping. We’ll take a candid look at the common reasons agent systems fail and introduce practical patterns to fix them using Haystack, an open-source framework purpose-built for production-grade LLM pipelines.

You’ll learn how to design agents that are:

• Modular, so they’re easy to extend and evolve
• Observable, so you can trace failures and understand behavior
• Maintainable, so they don’t become one-off science projects

We’ll also cover advanced topics like Model Context Protocol (MCP), Human-in-the-Loop to push your agents into more capable territory.

Whether you’re just starting to explore agents or trying to tame an unruly prototype, you’ll leave with a clear, actionable blueprint to build something that’s not just smart, but also reliable.