How to Find the Agent Failures Your Evals Miss with Scott Clark - #767

How to Find the Agent Failures Your Evals Miss with 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. The complete show notes for this episode can be found at https://twimlai.com/go/767.

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Avsnitt(789)

How AI Learns to Smell with Alex Wiltschko - #771

How AI Learns to Smell with Alex Wiltschko - #771

In this episode, Alex Wiltschko, founder and CEO of Osmo, joins the show to discuss his goal of giving computers a sense of smell and what it takes to build olfactory intelligence. We explore the sci...

8 Juli 59min

Why AI Agents Break the GenAI Security Model with Devvret Rishi - #770

Why AI Agents Break the GenAI Security Model with Devvret Rishi - #770

In this episode, Sam talks with Dev Rishi, GM of AI at Rubrik, about what happens when agents move beyond answering questions and start taking action across tools, systems, and business processes. We...

16 Juni 56min

Is RAG Dead? Lessons from Building AI for Tax Law with Alex Bowcut - #769

Is RAG Dead? Lessons from Building AI for Tax Law with Alex Bowcut - #769

As context windows grow into the millions of tokens, many AI practitioners are questioning whether retrieval-augmented generation (RAG) is still necessary. If modern models can ingest entire libraries...

9 Juni 51min

Relational Foundation Models for Enterprise Data with Jure Leskovec - #768

Relational Foundation Models for Enterprise Data with Jure Leskovec - #768

In this episode, Jure Leskovec, co-founder and chief scientist at Kumo and professor of computer science at Stanford, joins us to explore two fronts of his work: AI for science and relational deep lea...

21 Maj 1h 6min

How to Engineer AI Inference Systems with Philip Kiely - #766

How to Engineer AI Inference Systems with Philip Kiely - #766

In this episode, Philip Kiely, head of AI education at Baseten, joins us to unpack the fast-evolving discipline of inference engineering. We explore why inference has become the stickiest and most cri...

30 Apr 54min

How Capital One Delivers Multi-Agent Systems with Rashmi Shetty - #765

How Capital One Delivers Multi-Agent Systems with Rashmi Shetty - #765

In this episode, Rashmi Shetty, senior director of enterprise generative AI platform at Capital One, joins us to explore how the company is designing, deploying, and scaling multi-agent systems in a h...

16 Apr 54min

The Race to Production-Grade Diffusion LLMs with Stefano Ermon - #764

The Race to Production-Grade Diffusion LLMs with Stefano Ermon - #764

Today, we're joined by Stefano Ermon, associate professor at Stanford University and CEO of Inception Labs to discuss diffusion language models. We dig into how diffusion approaches—traditionally used...

26 Mars 1h 3min

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