How to Find the Agent Failures Your Evals Miss [Scott Clark] – 767

In this episode, Scott Clark, co-founder and CEO of Distributional, joins us to explore how teams can reliably operate and improve complex LLM systems and agents in production. Scott introduces a Maslow’s hierarchy of observability: telemetry for logging, monitoring for known signals, and post-production or online analytics to surface unknown unknowns. We dig into examples of real-world failures Scott’s team has seen in production systems, such as “lazy” tool-use hallucinations that standard evals miss, and how mapping traces into vector fingerprints enables clustering and topic discovery to uncover emergent behaviors. Scott explains how analytics can feed the data flywheel by generating evals, guardrails, and training data, and why online, adaptive approaches are essential for non-stationary models. We also touch on practical how-to’s such as instrumentation with OpenTelemetry, the GenAI semantic conventions, and the role of dedicated analytics tools.

🗒️ For the full list of resources for this episode, visit the show notes page: https://twimlai.com/go/767.

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🔗 LINKS & RESOURCES
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Distributional – https://www.distributional.com/
Distributional App – http://app.dbnl.com/
Distributional Docs – http://docs.dbnl.com/
Supporting Rapid Model Development at Two Sigma with Scott Clark & Matthew Adereth – 273 – https://twimlai.com/podcast/twimlai/supporting-rapid-model-development-at-two-sigma
Bayesian Optimization for Hyperparameter Tuning with Scott Clark – 50 – https://twimlai.com/podcast/twimlai/bayesian-optimization-for-hyperparameter-tuning
Democast: Automated Model Tuning with Scott Clark – https://twimlai.com/podcast/twimlai/automated-model-tuning

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