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

More from The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) All episodes →

1. 

   E770 · 16 Jun 2026 · 56 min

   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 explore why the enterprise playbook of static guardrails plus human approval starts to break down in the agent era. Agents are useful because they can plan, call tools, update systems, write code, send messages, and operate across workflows at machine speed, but those same capabilities make them difficult to govern with rules written in advance or approval prompts reviewed one at a time. Dev…

2. 

   E769 · 9 Jun 2026 · 52 min

   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 of documents, why bother with retrieval at all? In this episode, Alex Bowcut, Head of Engineering at Sphere, explains why the answer depends on the application. Sphere uses AI to automate global tax compliance—an environment where getting the answer right isn’t enough. Every conclusion must be backed by the correct legal citation, and every decision must withstand expert review. We explore how…

3. 

   E768 · 21 May 2026 · 1 hr 6 min

   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 learning. We begin with AI Virtual Cell, a multiscale effort to learn data-driven representations from proteins to cells to patients using single-cell RNA-seq data, protein language models like ESM, and structure models like AlphaFold—without hand-encoding biology. Jure then dives into relational deep learning, reframing enterprise databases as graphs and training neural networks directly on raw…

4. 

   E767 · 7 May 2026 · 53 min

   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…

5. 

   E766 · 30 Apr 2026 · 55 min

   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 critical workload in AI, how it blends GPU programming, applied research, and large-scale distributed systems, and where the line sits between inference and model serving. Philip shares how research-to-production can move in hours, not months, and why understanding “the knobs” of inference—batching, quantization, speculation, and KV cache reuse—lets teams design better products and SLAs. We trace…

6. 

   E764 · 26 Mar 2026 · 1 hr 3 min

   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 for images—are being adapted for text and code generation, the technical challenges of applying continuous methods to discrete token spaces, and how diffusion models compare to traditional autoregressive LLMs. Stefano introduces Mercury 2, a commercial-scale diffusion LLM that can generate multiple tokens simultaneously and achieve inference speeds 5-10x faster than small frontier models,…

7. 

   E763 · 10 Mar 2026 · 1 hr 16 min

   Agent Swarms and Knowledge Graphs for Autonomous Software Development with Siddhant Pardeshi - #763

   In this episode, Sid Pardeshi, co-founder and CTO of Blitzy, joins us to discuss building autonomous development systems able to deliver production-ready software at enterprise scale. Sid contrasts AI-assisted coding with end-to-end autonomy, arguing that “code is a commodity” and acceptance is the real metric—security, standards, tests, and maintainability included. We explore Blitzy’s hybrid graph-plus-vector approach, which grounds agents and combines semantic signals with keyword search to navigate large repositories efficiently. Sid breaks down context and agent engineering, how…

8. 

   E762 · 26 Feb 2026 · 1 hr 19 min

   AI Trends 2026: OpenClaw Agents, Reasoning LLMs, and More with Sebastian Raschka - #762

   In this episode, Sebastian Raschka, independent LLM researcher and author, joins us to break down how the LLM landscape has changed over the past year and what is likely to matter most in 2026. We discuss the shift from raw model scaling to reasoning-focused post-training, inference-time techniques, and better tool integration. Sebastian explains why methods like self-consistency, self-refinement, and verifiable-reward reinforcement learning have become central to progress in domains like math and coding, and where those approaches still fall short. We also explore agentic workflows in…

9. 

   E761 · 29 Jan 2026 · 1 hr 6 min

   The Evolution of Reasoning in Small Language Models with Yejin Choi - #761

   Today, we're joined by Yejin Choi, professor and senior fellow at Stanford University in the Computer Science Department and the Institute for Human-Centered AI (HAI). In this conversation, we explore Yejin’s recent work on making small language models reason more effectively. We discuss how high-quality, diverse data plays a central role in closing the intelligence gap between small and large models, and how combining synthetic data generation, imitation learning, and reinforcement learning can unlock stronger reasoning capabilities in smaller models. Yejin explains the risks of homogeneity…

10. 

    E760 · 8 Jan 2026 · 1 hr 7 min

    Intelligent Robots in 2026: Are We There Yet? with Nikita Rudin - #760

    Today, we're joined by Nikita Rudin, co-founder and CEO of Flexion Robotics to discuss the gap between current robotic capabilities and what’s required to deploy fully autonomous robots in the real world. Nikita explains how reinforcement learning and simulation have driven rapid progress in robot locomotion—and why locomotion is still far from “solved.” We dig into the sim2real gap, and how adding visual inputs introduces noise and significantly complicates sim-to-real transfer. We also explore the debate between end-to-end models and modular approaches, and why separating locomotion,…