Sep 20, 2026

When Should AI Systems Use Super Agents?

Grant Miller explains the paradox of "super agents" in AI systems – their immense power comes with significant risks. He details concerns like privilege abuse and expanded attack surfaces, contrasting them with the benefits of unified organizational intelligence and workflow coordination. The solution proposed is an architecture of "agent swarms," guided by principles of least agency, isolation, observability, and human oversight, to build secure and scalable agentic systems.

Sep 19, 2026

The Frontier AI Inference Cloud for Agents — Byung-Gon (Gon) Chun, FriendliAI

Byung-Gon Chun from FriendliAI details how agentic inference fundamentally differs from traditional chat-based LLM usage, requiring a specialized infrastructure to optimize for end-to-end task completion rather than single request latency. He highlights the economic advantage of open-weight models for agentic workflows and explains FriendliAI's innovative inference stack, featuring prefix caching, advanced KV cache management, cache-aware routing, and agent-aware optimization, which together deliver significant speedups and cost savings for production AI agents.

Sep 19, 2026

Routing LLM Inference in Production: From Engine Signals to Policy — Qianru Lao & Lu Zhang, OpenAI

This talk outlines OpenAI's journey in building a robust inference load balancer. It details the transition from an initial feedback-loop-driven system, which suffered from explainability issues and disruptive oscillations, to a sophisticated control plane/data plane architecture. The new system leverages a global view to optimize end-to-end latency across all traffic, balancing network distance with engine-side queuing, and includes critical protection mechanisms like outlier penalties, dynamic retry budgets, and load shedding to ensure stability in production.

Sep 19, 2026

Operating Distributed Inference Systems at Scale — Nishant Gupta & Naman Ahuja, Meta

Meta's Nishant Gupta and Naman Ahuja explain how AI inference, the company's fastest-growing workload, requires a dedicated orchestration layer akin to Kubernetes for virtual machines. They detail the unique challenges of LLM serving compared to traditional microservices, emphasizing the intricate coupling of infrastructure layers, the distributed transaction nature of requests, and a seven-axis framework for scheduling. The talk concludes with the imperative to optimize for "cost per successful task" and the necessity of an inference-specific control plane to manage the complex interplay of latency, cost, and throughput.

Sep 18, 2026

A Worm With 302 Neurons Inspired Their Architecture — Ramin Hasani, Liquid AI

Liquid AI began by drawing inspiration from the C. elegans worm to develop Liquid Neural Networks, focusing on continuous-time, differentiable systems for efficient intelligence. Facing scaling challenges, they developed STAR, a meta-AI system for architectural search, optimizing hybrid models for quality, memory, latency, and speed, leading to their Liquid Foundation Models (LFMs). Their core focus is on bringing AI to the edge (on-device, in-car) for cost reduction, privacy, and offline capabilities, with successful deployments at Mercedes-Benz and Shopify. Liquid is also building a platform for 'sovereign AI,' enabling enterprises to customize and own their intelligence end-to-end, and researching massively multimodal systems, efficiency across the AI stack, and novel data types like DNA.

Sep 18, 2026

Databricks CEO: Stop Scaring People About AI

Databricks CEO Ali Ghodsi discusses the current state of AI, arguing against existential risk alarmism and advocating for a focus on immediate threats like AI-powered cyberattacks. He provides a rigorous definition for recursive self-improvement and explains how building an organizational 'ontology' can unlock massive productivity gains in enterprises. Ghodsi also covers the shift to multi-model AI strategies and cost management, highlighting how infrastructure is evolving to support agentic systems.