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LangChain reports that since introducing dynamic scheduling for Managed Deep Agents in September, over 70% of MDA runs on LangSmith come from scheduled runs rather than manual prompts, framing proactive agents as easily deployable via `mda init` and `mda deploy`.
LangChain's Managed Deep Agents now function as MCP servers, allowing coding assistants and other agents to delegate tasks like research and analysis via a standard protocol endpoint.
LangChain announces the public beta of Managed Deep Agents, a managed runtime for deploying and scaling Deep Agents without managing infrastructure. It supports Python/TypeScript, local testing, one-command deployment, and integrates with LangSmith for production features like persistence, sandboxes, and evals.
LangChain announces Managed Deep Agents in private beta, a hosted API-first runtime for building, running, and operating deep agents in production, leveraging the open-source Deep Agents harness and integrating with LangSmith for durable execution, streaming, and human-in-the-loop workflows.
LangChain introduces Managed Deep Agents, maintaining a familiar project layout with AGENTS.md, skills/, subagents/, and tools.json, along with Context Hub for persistent context management across sessions.