Aug 25, 2025

Iterating on Your AI Evals // Mariana Prazeres // Agents in Production 2025

Moving an AI agent from a promising demo to a reliable product is challenging. This talk presents a startup-friendly, iterative process for building robust evaluation frameworks, emphasizing that you must iterate on the evaluations themselves—the metrics and the data—not just the prompts and models. It outlines a practical "crawl, walk, run" approach, starting with simple heuristics and scaling to an advanced system with automated checks and human-in-the-loop validation.

Aug 25, 2025

Integration of AI into Traditional Systems // Hakan Tek // Agents in Production 2025

This talk explores practical, low-disruption strategies for integrating AI capabilities into traditional and legacy enterprise systems without requiring a complete overhaul. It covers common challenges, effective integration patterns like API-based and hybrid approaches, and highlights readily available tools to help teams start small and deliver immediate business value.

Aug 20, 2025

Evaluation-Driven Development with MLflow 3.0

Yuki Watanabe from Databricks introduces Evaluation-Driven Development (EDD) as a critical methodology for building production-ready AI agents. This talk explores the five pillars of EDD and demonstrates how MLflow 3.0's new features—including one-line tracing, automated evaluation, human-in-the-loop feedback, and monitoring—provide a comprehensive toolkit to ensure agent quality and reliability.

Aug 19, 2025

Why Language Models Need a Lesson in Education

Stephanie Kirmer, a staff machine learning engineer at DataGrail, adapts her experience as a former professor to address the challenge of evaluating LLMs in production. She proposes a robust methodology using LLM-based evaluators guided by rigorous, human-calibrated rubrics to bring objectivity and scalability to the subjective task of assessing text generation quality.

Aug 13, 2025

EDD: The Science of Improving AI Agents // Shahul Elavakkattil Shereef // Agents in Production 2025

This talk introduces Eval-Driven Development (EDD) as a scientific alternative to 'vibe-based' iteration for improving AI agents. It covers quantitative evaluation (choosing strong end-to-end metrics, aligning LLM judges) and qualitative evaluation (using error and attribution analysis to debug failures), providing a structured framework for consistent agent improvement.

Aug 13, 2025

When Agents Hire Their Own Team: Inside Hypermode’s Concierge // Ryan Fox-Tyler

Ryan Fox-Tyler from Hypermode explains their philosophy of empowering AI agents to design and deploy other agents. He introduces Concierge, an agent that builds other agents, and details the underlying actor-based runtime built for scalability, fault tolerance, and efficient, event-driven execution of thousands of parallel agent instances.