Topic
Dynamic Code Execution
6 episodes
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Overview: Dynamic Code Execution
We finally slow down and explain dynamic code execution from the ground up — what it actually is, how the loop works, why it makes models meaningfully more capable, and where the real costs and failure modes live.
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Code as Worlds: Agentic Discovery of Executable World Representations for Physical Reasoning
Code-as-World represents physical worlds as executable code—objects, dynamics, and visual appearance all expressed as runnable specifications. An agent discovers these representations through a propose-execute-render-verify loop: hypothesize a world in code, run it in a simulator, check the outputs against video or language evidence, and refine. The result is quantitatively grounded supervision for training vision-language models on physical reasoning tasks like measuring velocity and displacement from video. Code-as-World-VL outperforms larger proprietary models on QuantiPhy benchmarks.
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Why managed agents are the next big thing in agent building
Jessica and Cathy dig into Harrison Chase’s argument that “managed agents” are the next stage of agent building, where harness plus infra are bundled and driven by emerging standards like AGENTS dot md, MCP, and skills. They unpack what problems this actually solves in production, how LangChain’s Managed Deep Agents fits into their running harness-as-product thesis, where the piece is strongest, and what’s still underspecified.
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Managed Deep Agents is now in public beta
Jessica and Cathy discuss LangSmith Managed Deep Agents entering public beta, exploring its value for shipping agents without managing infrastructure, its open-source harness, and how it fits into the broader agent landscape including Anthropic's competing offering.
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MCP server portals
Asteria and Draco unpack Cloudflare's MCP server portals as boundary infrastructure for enterprise MCP adoption: one Access-controlled endpoint, curated tools, managed OAuth, Code Mode, and observability, with caveats around direct server URLs, admin credentials, and sync paths.
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How to Use RLMs in Deep Agents
Exploring Recursive Language Models (RLMs) and their implementation in Deep Agents for handling long contexts efficiently.