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How to Optimize AI Agents in Production

How to Optimize AI Agents in Production

Engineers building AI agents face a combinatorial explosion of configuration choices (prompts, models, parameters), leading to guesswork and suboptimal results. This talk introduces a structured, data-driven approach using multi-objective optimization to systematically explore this vast design space. Learn how the Traigent SDK helps engineers efficiently identify optimal tradeoffs between cost, latency, and accuracy, yielding significant quality improvements and cost reductions without manual trial-and-error.

Evaluating AI Agents: Why It Matters and How We Do It

Evaluating AI Agents: Why It Matters and How We Do It

Annie Condon and Jeff Groom from Acre Security detail their practical approach to robustly evaluating non-deterministic AI agents. They share their philosophy that evaluations are critical for quality, introduce their "X-ray machine" analogy for observability, and walk through their evaluation stack, including versioning strategies and the use of tools like Logfire for tracing and Confident AI (Deep Evals) for systematic metric tracking.

Designing AI Agents for the Complex Realities of Healthcare

Designing AI Agents for the Complex Realities of Healthcare

Dr. Sarah Gebauer presents a clinical framework for deploying AI agents in healthcare, drawing a powerful analogy between AI agents and medical residents. She outlines the critical risks, validation strategies, and post-deployment monitoring required to make agents useful, safe, and credible in high-stakes clinical environments.

Building Multi-Player AI Systems (and why it’s SO hard)

Building Multi-Player AI Systems (and why it’s SO hard)

MeshAgent introduces a multiplayer AI paradigm, shifting from single-user systems to collaborative 'Rooms' where teams of humans and agents can work together with shared context. This talk explores the platform's architecture, developer tools, and its approach to solving real-world collaborative tasks.

AI Needs Memory - Here's How It Works

AI Needs Memory - Here's How It Works

A deep dive into the architectural and economic foundations of memory for AI agents. The talk explores the core tradeoffs between classical data storage and dynamic agent behavior, introduces a human-inspired framework for memory, and discusses practical strategies and future directions for building reliable, evolving AI systems.

Trust at Scale: Security and Governance for Open Source Models // Hudson Buzby // MLOps Podcast #338

Trust at Scale: Security and Governance for Open Source Models // Hudson Buzby // MLOps Podcast #338

Hudson Buzby from JFrog discusses the critical security, governance, and legal challenges enterprises face when adopting open-source AI models. He highlights the risks lurking in repositories like Hugging Face and argues for a centralized, curated AI gateway as the essential framework for enabling safe, scalable, and cost-effective AI development.

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