> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-harris-1786029617-6b0a55e.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Managed Deep Agents

> Overview of Managed Deep Agents public beta features, workflows, and limits.

Managed Deep Agents is simpler way to create powerful agents.
It uses a [powerful agent harness](/oss/python/deepagents/overview) with opinionated infrastructure, allowing you to focus on the your business logic.

Managed Deep Agents consist of three layers:

* Business logic - this is what you are in charge of
* Agent Harness - we use the [Deep Agents](/oss/python/deepagents/overview) harness
* Opinionated Infrastructure - this is what managed Deep Agents adds over the open source Deep Agents Harness

The opinionated infrastructure consists of several pieces:

**Runtime:** We use LangSmith Agent Server to run agents in a durable, fault tolerant manner

**Sandboxes:** We us LangSmith Sandboxes so agents can write and execute untrusted code in a secure manner

**Evals:** We make it easy to author evals in Harbor format, so you can properly test agents

**UX:** We have a "channels" abstraction to allow you to easily bring your agent to platforms where your users live.

**Memory:** We have opinionated memory so that your agents can remember interactions.

**Context Management:** We use LangSmith Context Hub to manage agent instructions and skills so that you can modify them in a UI and have those changes take affect without having to redeploy.

***

<div className="source-links">
  <Callout icon="terminal-2">
    [Connect these docs](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers.
  </Callout>

  <Callout icon="edit">
    [Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/langsmith/managed-deep-agents-overview.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).
  </Callout>
</div>