Oct 09, 2026

Why 80% Reliability Isn't Good Enough — Felipe Blanes, Amazon AGI Lab

Felipe Blanes from the Amazon AGI Lab shares lessons from working directly with customers of Nova Act, Amazon's browser agent service, from research preview to general availability on AWS. He argues that static benchmarks leave teams hoping their evals match real usage, and proposes a customer-driven eval flywheel, the trust cliff around 90% reliability, the value of being open about limits, and principles for keeping evals fresh.

Oct 08, 2026

Brains vs Hands: How to Run AI Agents Safely in Production — Viren Baraiya

Viren Baraiya, CTO of Orkes and original creator of Netflix Conductor, explains why production agents behave like applications built from microservices. He argues for splitting the work between a non-deterministic LLM that plans and a deterministic, durable harness that executes, with approval gates, idempotent steps, and recorded side effects. He frames agent harnesses as late-bound sagas and demos an SRE remediation agent whose plan is compiled into a Conductor workflow.

Oct 08, 2026

Data Architecture for AI: From Training Pipelines to Agent Memory

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.

Oct 08, 2026

Inside a Game Where AI Agents Never Sleep

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.

Oct 08, 2026

Building the Cloud for AI Agents | AWS CEO Matt Garman

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.

Oct 08, 2026

Giving AI Agents Memory That Learns — Jake Broekhuizen, LangChain

Jake Broekhuizen of LangChain Labs argues that observability alone does not make agents improve. Using a financial services agent whose tone drifted from advisory to pushy, he shows how traces only become useful memory when a filtered lesson is written back as durable context the agent reads on its next run. The talk covers semantic, episodic, and procedural memory, the working versus long-term memory distinction, the read-write cycle, and how LangSmith and Context Hub support capture, analysis, and updates, along with lessons about keeping only signal, caching, and human review of procedural changes.