Vanishing Gradients Episode 61: The AI Agent Reliability Cliff: What Happens When Tools Fail in Production
35 snips
Oct 16, 2025 In a fascinating discussion, Alex Strick van Linschoten, a machine learning engineer at ZenML and curator of the LLM Ops Database, delves into the complexities of multi-agent systems. He emphasizes the dangers of introducing too many agents, advocating for simplicity and reliability. Alex shares key insights from nearly 1,000 real-world deployments, highlighting the importance of MLOps hygiene, human-in-the-loop strategies, and using basic programming checks over costly LLM judges. His practical advice on scaling down systems is a must-listen for AI developers!
AI Snips
Chapters
Transcript
Episode notes
Keep Agent Systems Extremely Narrow
- Do keep agent systems as simple and narrow as possible to avoid chaos from premature scaling.
- Prefer small, focused use cases over adding many autonomous components that are hard to manage.
Instrument Everything And Run Continuous Evals
- Do implement basic MLOps hygiene: trace everything and run continuous evaluations to find failures.
- Use those traces as the core feedback loop to debug and improve models and agents.
Quality In, Quality Out For Agents
- High-quality context and inputs are essential: garbage in yields poor agent outputs.
- Improving retrieval and prompt context often yields bigger gains than tweaking models.