Aug 12, 2026

RL Environments Explained: How AI Agents Learn Real-World Work | Brendan Foody, Mercor

Mercor CEO Brendan Foody elucidates the concept of RL environments, essential for training advanced AI agents. He breaks down their three core components—worlds, apps, and tasks—and details Mercor's evolution from crowdsourced data to expert-driven, "agentic" data. Foody underscores the indispensable role of human experts in defining frontier tasks and creating robust verifiers, exemplified by a real legal RL environment. He shares post-training results demonstrating significant performance gains with modest compute, discusses data pricing and quality, demystifies synthetic data, and explores future directions like ultra-long-horizon tasks and virtual co-workers. The talk emphasizes that data sets are becoming a critical moat for application-layer companies, enabling them to own their intelligence.

Aug 11, 2026

Evolution of agentic surfaces — Gagan Bhat & Isabella Kai He, Anthropic

Anthropic's Gagan Bhat and Isabella Kai He discuss how agent harnesses must evolve rapidly to keep pace with fast-improving LLMs. They introduce Claude Managed Agents, an architecture that decouples the agent's 'brain' (reasoning) from its 'hands' (tool execution) to address issues like stale assumptions, latency, and reliability. This approach enables dynamic adaptation, secure tool execution, and features like 'dreaming' for self-improving agents and 'outcomes' for goal-oriented task completion, ultimately aiming to close the gap between model capabilities and product offerings.

Aug 08, 2026

Anthropic's CCA Exam as a Field-Guide for Agentic Engineering — Frank Coyle, UC Berkeley

Frank Coyle demystifies the Claude Certified Architect exam by dissecting key scenarios and highlighting common anti-patterns in Agentic AI design. He provides actionable best practices, emphasizing effective tool use, context management, specialized agent architectures, and cost-saving techniques, all centered on understanding what to avoid to build robust LLM applications.

Aug 08, 2026

Why Robotics Still Isn't Solved - But Could Be Soon | YC Paper Club

This Paper Club delves into the current state of robotics, addressing roadblocks like the sim-to-real gap and embodiment drift. Speakers present advancements in multi-scale memory for long-horizon tasks, self-supervised embodied reasoning, zero-shot dexterous manipulation via massive simulation, and the economic imperative of teleoperation-first robotics companies, concluding with optimizations for efficient, real-time World Action Models.

Jul 23, 2026

Why Agentic Systems Need Ontologies — Frank Coyle, UC Berkeley

Frank Coyle discusses the inherent flaws of purely probabilistic LLMs in agentic systems and proposes a neuro-symbolic solution. He advocates for wrapping LLM tool-use loops with external ontologies and validation mechanisms (like RDFS, OWL, and Pydantic) to provide logical guardrails, preventing common, critical errors that probabilistic reasoning alone cannot reliably avoid.

Jul 14, 2026

5 More AI Myths & The Truth Behind Them: ML, Context, Agents & More

Martin Keen debunks five common AI myths, covering topics from reduced AI hallucinations and the misinterpretation of AI's "thinking" process, to the rising costs of AI inference, the limitations of large context windows, and the current challenges to fully autonomous AI agents.