2025-09-02 –, B07-B08
AI agents are having a moment, but most of them are little more than fragile prototypes that break under pressure. Together, we’ll explore why so many agentic systems fail in practice, and how to fix that with real engineering principles. In this talk, you’ll learn how to build agents that are modular, observable, and ready for production. If you’re tired of LLM demos that don’t deliver, this talk is your blueprint for building agents that actually work.
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 Python framework to build custom AI applications.
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 the behavior
- Maintainable, so they don’t become one-off science projects
We’ll also cover advanced topics like multimodal inputs and Model Context Protocol (MCP) 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.
Familarity with basic concepts in generative AI like LLMs and prompting
Basic python knowledge
Novice
Abstract as a tweet (X) or toot (Mastodon):Most AI agents fail because they’re not engineered for scale, no observability, no modularity, no testing. At #PyDataBerlin, @bilgeycl will show how to architect robust, production-grade agents using @deepset_ai’s @Haystack_AI, tracing, MCP, and more.
Bilge is a developer relations engineer at deepset, where she helps developers build powerful AI applications and teaches the world how to use Haystack. Passionate about RAG, LLMs, and all things Gen AI, she enjoys making complex AI concepts accessible both online and at real-life events