Matt Gibiec of Dynatrace argues that coding agents are fast but blind without real context about how services run in production, and introduces Bluebox, an observability agent that brings dependency maps, production baselines, and change history into local testing, issue detection, fix suggestions, and pre-build planning, with a human approving the final result.
Alan Kourev, co-founder and CTO of VAST Data, explains how AI workloads (training, inference with KV cache offload, RAG, and agents) access data in fundamentally different ways, and presents a disaggregated, shared-everything architecture, shaped in part by work with CoreWeave, that serves all of them from one unified platform.
Isaac Lee of NEXUS explains why he wants to fill MMORPGs with agent players instead of scripted NPCs to bootstrap multiplayer worlds, and walks through NEXUS's AI lab: text-to-3D world building, novel-to-video pipelines, pixel animation loops, the Da Vinci multi-agent dev framework, self-hosted inference after Kimi, the Bob meeting assistant, and the implications of agentic shopping for e-commerce.
Abhi Arya from Reducto shares how the team went from a one-tool-per-endpoint MCP server that confidently built workflows nobody could explain to a design that makes uncertainty visible, asks instead of guessing, and improves itself through instrumentation. The talk centers on one question: who pays when the agent is wrong?
NVIDIA presents Vera, a CPU designed for agentic AI workloads such as tool calls, code execution, sandboxes and reinforcement learning loops. The talk traces how cloud economics shaped CPU design toward high core counts at the expense of per-core speed, then explains how Vera's Olympus core, monolithic fabric and LPDDR5X memory deliver high single-threaded performance at scale. CoreWeave then shares its early results running Vera for sandboxes and agent workloads.
a16z's Raghu Raghuram interviews AWS CEO Matt Garman about how AI is reshaping cloud infrastructure: why startups remain central to AWS, how cloud primitives like databases, permissions, and sandboxes must be rethought for agents, how GPU capacity is allocated amid $220B in capex, and how Graviton and Trainium fit into the AI stack. The discussion also covers enterprise barriers to autonomous agents, data privacy with Bedrock, and AWS's own internal use of agents.