This November Reading Group conversation covers advanced retrieval techniques, strategies like iter-drag and hyper-drag for complex queries, and the impact of larger context windows on model performance. The Reading Group also examines challenges in generalizing these methods.

Matt Van Itallie, founder and CEO of Sema, discusses how comprehensive codebase evaluations play a crucial role in MLOps and technical due diligence. He highlights the impact of Generative AI on code transparency and explains the Generative AI Bill of Materials (GBOM), which helps identify and manage risks in AI-generated code. This talk offers practical insights for technical and non-technical audiences, showing how proper diligence can enhance value and mitigate risks in machine learning operations.

Explore the role in boosting model performance, on-device AI processing, and collaborations with tech giants like ARM and Apple. Michael shares his journey from gaming console accelerators to AI, emphasizing the power of community and innovation in driving advancements.

In an era where knowledge workers are inundated with manual tasks and corporate memory systems, agents hold massive potential to streamline and document their workflows. Agents also hold the potential to unlock a vast amount of skills and knowledge from the front-line employees of global businesses. This talk explores how dynamic agents can capture work patterns, from inboxes to task sequences, using clustering and process mining techniques. These methods ground agentic planning and provide more robust, company-specific workflow orchestration. We’ll also discuss how knowledge graphs power effective memory management, and how Interloom’s innovative MemoryRank™ system leverages reflective agents to create and serve agentic memories, optimizing task management for both human workers and specialized agents. Attendees will gain insights into how agentic memory systems can drive efficiency and productivity for end-to-end processes and workflows.

The Creative Singularity is here! Welcome to the age of transformable media, where audiences can generate and remix their entertainment at will – and AI systems will regenerate the content in return. This talk will take you behind the scenes on several of Transitional Forms' dynamic media projects, designed to use machine intelligence as a real-time creative collaborator to generate the content itself.

In a world dominated by Python-centric AI frameworks, JavaScript developers have often found themselves at a disadvantage when it comes to building intelligent, multi-agent systems. KaibanJS aims to change that. In this talk, we’ll explore how this first-of-its-kind, JavaScript-native framework enables developers to create and integrate AI agents effortlessly into their projects. We’ll break down real-world use cases, dig into the technical features, and demonstrate how KaibanJS is helping to bridge the gap for JavaScript developers in the AI space.

As conversational AI systems become increasingly autonomous and mission-critical, ensuring their reliability presents novel challenges that parallel those faced in self-driving vehicle development. This talk explores how simulation-based testing and evaluation strategies from autonomous vehicles can be adapted to build more robust AI agents. We'll examine how traditional software engineering practices are evolving in response to AI systems, where deterministic unit tests give way to probabilistic performance metrics and behavioral analysis. Drawing from real-world examples, we'll demonstrate how comprehensive telemetry — both in pre-production simulation and production environments — provides crucial insights beyond simple pass/fail metrics. The presentation will delve into the critical balance between computational cost, latency requirements, and signal quality in AI system evaluation. We'll introduce a framework for developing evaluation strategies based on reliability requirements across different use cases, from bug detection tools where any true positive provides value, to medical assistance systems that demand near-perfect accuracy. Attendees will learn practical approaches to implementing simulation-based testing pipelines, techniques for meaningful telemetry collection and analysis, and strategies for defining appropriate reliability thresholds based on application context. This session will benefit ML engineers, software architects, and engineering leaders working on production AI systems.

AI agents change what we can achieve with software; but also change how we have to think about building software systems. In this talk, I'll share some of the lessons we've learned while building a powerful AI agent for complex support settings.