A summary of the keynote by Lukas Biewald (Weights & Biases) and Camille Fournier (CoreWeave) at Fully Connected London 2025. They discuss recent product updates for W&B Models and Weave, the synergy behind the CoreWeave acquisition, and a deep dive into building and automating an autonomous software engineer agent.
Jeff Huber, CEO of Chroma, discusses "context rot," the degradation of AI performance in large context windows, and outlines a new vision for retrieval infrastructure. He covers the evolution of search, the importance of a two-stage recall-then-precision pipeline, and the challenges of agentic memory, advocating for a shift from AI "alchemy" to reliable engineering.
Zhang Yuxuan from Z.ai details the technical roadmap behind the GLM-4.6 model series, which has achieved top performance on the LMSYS Chatbot Arena. The summary covers their 15T token data recipe, the SLIME framework for efficient agent RL, key lessons in single-stage long-context training, and the architecture of the multimodal GLM-4.5V model.
Sam Partee, CTO of Arcade, explains the critical gap between AI agents that gather context and those that take secure, real-world actions. He introduces Arcade as a middleware platform that solves complex challenges like user authorization, fine-grained permissions, and token management, enabling developers to build scalable, enterprise-ready agents.
AI agents represent a paradigm shift from traditional reactive AI models. This summary explores their proactive, goal-driven nature, detailing how they autonomously plan and execute complex workflows by interacting with a diverse ecosystem of models, APIs, hardware, and even other agents to solve real-world problems.
Aman Khan from Arize discusses the challenges of building reliable AI agents and introduces a novel technique called "metaprompting". This method uses continuous, natural language feedback to optimize an agent's system prompt, effectively training its "memory" or context, leading to significant performance gains even for smaller models.