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This is a hosted runtime for deep agents: LLM agents with a filesystem, planning, and subagents, deployed with durable threads, sandboxes, memory, and scheduled runs on LangSmith Cloud. The article is a product announcement with its own claims, such as the 80% code reduction in Content Studio, so those are unverified. For AI readers, the practical question is how much lock-in and operational control a managed agent runtime trades for faster deployment.
Managed Deep Agents: the fastest way to ship a production deep agent
Run deep agents in production with durable execution, sandboxes, tool access, and LangSmith observability, without building the runtime yourself. Now in private beta
Updated November 2, 2026. Managed Deep Agents is in public beta on LangSmith Cloud, and this post now describes how it works today. It first ran on May 13, 2026, when we opened a private beta. Managed Deep Agents lets you build a deep agent as a folder of files and deploy it to LangSmith with one command. You write the agent's instructions and tools and pick the model, and LangSmith runs the Deep Agents harness and the production runtime around it. There are no servers to run and no infrastructure to wire together. A support agent, for example, can answer questions in a Slack channel and sweep stale tickets every night, with each of those declared as a file in its project. Managed Deep Agents…
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