Hugo speaks with Alex Strick van Linschoten, Machine Learning Engineer at ZenML and creator of a comprehensive LLMOps database documenting over 400 deployments. Alex's extensive research into real-world LLM implementations gives him unique insight into what actually works—and what doesn't—when deploying AI agents in production.

In this episode, we dive into:

The current state of AI agents in production, from successes to common failure modes
Practical lessons learned from analyzing hundreds of real-world LLM deployments
How companies like Anthropic, Klarna, and Dropbox are using patterns like ReAct, RAG, and microservices to build reliable systems
The evolution of LLM capabilities, from expanding context windows to multimodal applications
Why most companies still prefer structured workflows over fully autonomous agents

We also explore real-world case studies of production hurdles, including cascading failures, API misfires, and hallucination challenges. Alex shares concrete strategies for integrating LLMs into your pipelines while maintaining reliability and control.

Whether you're scaling agents or building LLM-powered systems, this episode offers practical insights for navigating the complex landscape of LLMOps in 2025.

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