In this special year-end episode, experts from the Mixture of Experts podcast review the biggest AI moments of 2025 and predict what's next for 2026. The discussion covers the rise of "super agents" and the battle for the user interface, open source's breakout year and its remaining challenges, the AI hardware supply crisis and the push for efficiency, and the future of modular, multimodal AI systems.
A deep dive into building agentic systems using OpenAI's latest APIs. The session covers the core concept of 'agentic tool calling' (reasoning + tools), outlines a four-part framework (Agent, Infrastructure, Product, Evaluation) for designing long-horizon tasks, and provides a hands-on demonstration of building a non-blocking task processing system with a real-time progress UI.
Lak Lakshmanan presents a robust architecture for building production-quality, framework-agnostic agentic systems. He advocates for using simple, composable GenAI patterns, off-the-shelf tools for governance, and a strong emphasis on a human-in-the-loop design to create continuously learning systems that avoid vendor lock-in.
Shraddha Yeole from Cisco ThousandEyes explains how they are transforming network observability by moving from complex dashboards to AI-augmented storytelling. The session details their use of an LLM-powered agent to interpret vast telemetry data, accelerate fault isolation, and improve MTTR, covering the technical architecture, advanced prompt engineering techniques, evaluation strategies, and key challenges.
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.
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.