Oct 13, 2026

Empowering Intelligence: democratizing data access and driving smarter decisions at Nasdaq

A NASDAQ executive explains how high-performance compute and agentic AI are reshaping both the physical trading infrastructure (data centers, colocation, liquid cooling, and CoreWeave partnership) and the fintech software stack (Verafin, Axiom, Calypso) through a shared intelligence layer. The talk centers on trust, resilience, governance and the bounded-exception problem that makes agentic workflows hard to scale in a regulated market.

Sep 18, 2026

Total Recall: Agent Memory and Harness Engineering — Ignacio Martinez, Oracle

Ignacio Martinez's workshop delves into agent harness engineering, emphasizing that while LLMs are a 'frozen' reasoning core, the surrounding harness is where control and reliability are established. He outlines a seven-layer architecture for building robust agent harnesses, covering critical aspects like hybrid storage solutions, advanced memory engineering (including in-database embeddings and context card management), the semantic layer (Umwelt), agent loops, and context engineering. The session also explores continual learning through 'skill promotion' and addresses practical challenges like managing vast tool sets and optimizing model interactions.

Sep 14, 2026

How We Solved Agent Building — Andrew Qu, Vercel

Andrew Qu, Chief of Software at Vercel, details the year-long journey of building an internal data science agent, from its initial "awful" state to the release of Eve, a framework for agent development. He covers iterative architectural improvements, the critical "file system unlock" inspired by Claude Code, and the distillation of common queries into reusable "skills," culminating in a framework that simplifies the creation of specialized, company-specific AI agents.

Sep 04, 2026

In Case You Missed It in August 2026 (Ep. 1024 with Jon Krohn)

This episode explores the critical gap between AI investment and return, featuring insights from industry leaders. It covers why most organizations fail to see ROI from AI, the crucial role of mathematical optimization in agentic AI to ensure constraint adherence, practical strategies for structuring AI skills in tools like Claude for quality output, and a 10-20-70 budget framework emphasizing upskilling. Additionally, it delves into the growing importance of the semantic layer for analytics agents to ensure consistent data interpretation and efficiency in large enterprises.

Aug 25, 2026

The 12 KB File That Replaces Weeks of Training (with Tristan Handy)

Tristan Handy, founder and CEO of dbt Labs, details the evolution of analytics engineering from a 2016 study into a tool used by over 100,000 data teams. He explains his decision to use SQL over Spark for accessibility, the concept of "progressive complexity," and how dbt projects transform raw data into modeled tables using a Directed Acyclic Graph. Handy elaborates on the critical role of the semantic layer in ensuring consistent metric definitions for both human users and AI agents, especially in large organizations. He introduces the dbt Fusion Engine, aiming to bring type safety and universal SQL understanding, and discusses how 12-kilobyte skill files can revolutionize large-scale dbt migrations, reducing them from years to weeks by enabling AI agents to absorb vast amounts of expert knowledge.

Jul 30, 2026

Let's integrate AI Agents in Event-Sourced Systems — Divakar Kumar, FlyersSoft

This presentation explores integrating AI agents into existing event-sourced architectures to resolve ambiguous cases in real-time fraud detection. By leveraging a semantic layer built from various bounded contexts (transaction, device, account), specialized agents like Risk Analyzer and Behavior Analyzer use tools and short-term memory to reach a verdict, addressing the "gray zone" where traditional rule-based and ML systems fall short. The approach emphasizes layering agents without replacing existing infrastructure, enhancing judgment in production systems.