# Managed Deep Agents

Deep Agents adds async subagents: an orchestrator stays in the foreground talking to the user and organizing context, while subagents run long tasks as background workers and report back on completion. Previously, in a standard ReAct loop, each long tool call blocked the loop and every other task queued behind it; now subagents no longer hold the orchestrator's loop, the user can reach it whenever its own loop isn't busy, and the work an agent can take on grows with the number of background workers rather than the length of one loop.

Deep Agents adds async subagents: an orchestrator stays in the foreground talking to the user and organizing context, while subagents run long tasks as background workers and report back on completion. Previously, in a standard ReAct loop, each long tool call blocked the loop and every other task queued behind it; now subagents no longer hold the orchestrator's loop, the user can reach it whenever its own loop isn't busy, and the work an agent can take on grows with the number of background workers rather than the length of one loop.

Managed Deep Agents in LangSmith now hosts a browser for agents and supplies four tools for snapshotting, batch actions, screenshots and handoff to a person, so agents can click, scroll and fill forms instead of relying on APIs. On 100 everyday tasks from WebVoyager on live websites, the toolset passed 90% for $1.98 in total, the highest pass rate among seven toolsets tested; the closest, Browserbase's MCP server, passed 89% at 9x the cost. Browser sessions can be scoped per thread or per user, and an agent can hand the browser to a person at a login page and resume afterward.

Managed Deep Agents in LangSmith now hosts a browser for agents and supplies four tools for snapshotting, batch actions, screenshots and handoff to a person, so agents can click, scroll and fill forms instead of relying on APIs. On 100 everyday tasks from WebVoyager on live websites, the toolset passed 90% for $1.98 in total, the highest pass rate among seven toolsets tested; the closest, Browserbase's MCP server, passed 89% at 9x the cost. Browser sessions can be scoped per thread or per user, and an agent can hand the browser to a person at a login page and resume afterward.

LangChain has published how it built Content Studio, its in-house design agent: the model could already write HTML, CSS and animation timelines but lacked the company's own design judgment, so output drifted toward familiar patterns. The team wrote designers' principles, brand rules and the reasoning behind feedback into skills and a wiki, and gave the agent a sandbox computer that keeps a project across a Slack thread plus asynchronous subagents. The agent has delivered more than 3,000 assets, and median model spend per request fell from about $8 in mid-September to about 50 cents.

LangChain has published how it built Content Studio, its in-house design agent: the model could already write HTML, CSS and animation timelines but lacked the company's own design judgment, so output drifted toward familiar patterns. The team wrote designers' principles, brand rules and the reasoning behind feedback into skills and a wiki, and gave the agent a sandbox computer that keeps a project across a Slack thread plus asynchronous subagents. The agent has delivered more than 3,000 assets, and median model spend per request fell from about $8 in mid-September to about 50 cents.

LangChain released a set of LangSmith features at Interrupt NYC and London so teams can own their intelligence in production. Previously, finding conversations meant relying on traditional filters; AI Search now retrieves traces by semantic understanding, so a team can ask whether a user got frustrated or whether PII leaked. The same update adds Managed Deep Agents v0.9, Engine v2 with red teaming, Trajectories, Custom Apps, and Fine-Tuning, covering how agents are built, understood, and improved.

LangChain released a set of LangSmith features at Interrupt NYC and London so teams can own their intelligence in production. Previously, finding conversations meant relying on traditional filters; AI Search now retrieves traces by semantic understanding, so a team can ask whether a user got frustrated or whether PII leaked. The same update adds Managed Deep Agents v0.9, Engine v2 with red teaming, Trajectories, Custom Apps, and Fine-Tuning, covering how agents are built, understood, and improved.