Jul 29, 2026

Your Agent Didn't Fail. Your Harness Did. — Vinoth Govindarajan, OpenAI

Vinoth Govindarajan's talk addresses critical 'harness failures' in AI agents, arguing these, rather than model errors, are the root cause of most production incidents. He introduces the core contract: 'A model proposes, the harness commits, and a receipt proves it,' and outlines five key boundaries (state ownership, ordering, deadlines, authority, user-visible proof) that lead to failures like silent success and incomplete reality. The talk culminates in a practical 'run receipt audit' with five questions to diagnose and ensure reliable agent behavior.

Jul 25, 2026

From Agent Traces to Agent Simulations — Rustem Feyzkhanov, Snorkel AI

Rustem Feyzkhanov discusses the critical need for companies to build private, production-aligned benchmarks for AI agents. He explains how to turn agent traces into repeatable simulations, why public benchmarks are insufficient, and how a CI pipeline for agents, integrating observability and experimentation, can ensure reliable evaluation, continuous improvement, and effective release management, moving beyond simple pass rates to measure cost, latency, and policy adherence.

Jul 20, 2026

The First Dedicated YC GPU Cluster - With Together AI

YC and Together AI have partnered to launch the first dedicated YC GPU cluster, addressing the critical compute bottleneck faced by AI-native startups. This initiative provides flexible, cost-effective access to GPU resources, enabling companies from early-stage research to major players to train, fine-tune, and run inference on AI models, and mitigating the financial strain of long-term compute commitments.

Jul 11, 2026

Develop at Idea Velocity - Jeffrey Lee-Chan, Snapchat

Jeffrey Lee-Chan argues that the critical component for production AI agent systems is not the model but the "harness"—an orchestration layer providing persistent memory and contextual guardrails. He introduces Open Claw, a conceptual framework that separates agent orchestrator managers (handling task specs and history) from specialized workers (executing implementation details). This architecture enables deterministic teamwork, parallelization, and unbiased outcomes, supported by examples like AI RPGs and multi-AI analysis, alongside tools like tmux for enhanced developer productivity and careful token management strategies.

Jul 09, 2026

Agentic AI Frameworks Explained: Workflows, Multi-Agent, & Production

This video tackles the overwhelming choice of agentic AI frameworks by categorizing projects into five types: linear workflows, autonomous multi-agent systems, role-based AI, production orchestration, and rapid prototyping. It details each type with examples and recommends specific frameworks like LangChain, AutoGen, and CrewAI, guiding developers to select the optimal tool based on their system design and real-world needs.

May 20, 2026

Building MCP Before MCP Existed: Inside Despegar's Sofia Agent

A deep dive into Despegar's GenAI travel agent, Sofia. Explore its multi-agent architecture, the custom orchestration layer 'Chappi' built before MCP was a standard, and the strategy of decentralizing agent development across company squads to cover the entire five-phase travel arc.