Episode 43: Tales from 400+ LLM Deployments: Building Reliable AI Agents in Production

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Vanishing Gradients

Hugo Bowne-Anderson

62 episodes

6 days ago

A podcast about all things data, brought to you by data scientist Hugo Bowne-Anderson. It's time for more critical conversations about the challenges in our industry in order to build better compasses for the solution space! To this end, this podcast will consist of long-format conversations between Hugo and other people who work broadly in the data science, machine learning, and AI spaces. We'll dive deep into all the moving parts of the data world, so if you're new to the space, you'll have an opportunity to learn from the experts. And if you've been around for a while, you'll find out what's happening in many other parts of the data world.

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All content for Vanishing Gradients is the property of Hugo Bowne-Anderson and is served directly from their servers with no modification, redirects, or rehosting. The podcast is not affiliated with or endorsed by Podjoint in any way.

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Episode 43: Tales from 400+ LLM Deployments: Building Reliable AI Agents in Production

Vanishing Gradients

1 hour 1 minute 3 seconds

9 months ago

Episode 43: Tales from 400+ LLM Deployments: Building Reliable AI Agents in Production

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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