New in LangSmith: Engine v2, Managed Deep Agents, Fine-Tuning, and More

LangSmith announced a set of releases at Interrupt NYC aimed at helping teams build, deploy, govern, and improve domain-specific AI agents through enhanced runtime infrastructure, observability, evaluations, and an intelligence layer that uses production data. LangSmith Engine v2 adds proactive red teaming, broader detection of reliability, latency, cost, and inefficient-tool-use issues, plus automated validation of candidate fixes before users create deployment pull requests. Managed Deep Agents v0.8 introduces identity-scoped user memory, separated access controls, agent- and user-owned external credentials, expanded Slack file sharing, HTTP webhook channels, and built-in web search powered by Parallel. LangSmith Trajectories provides a chronological, readable view of agent sessions across agents, tools, and subagents to simplify debugging, expert review, annotation, and online evaluation. LangSmith Fine-Tuning and its smithtune CLI support supervised fine-tuning of open models using trajectory-derived datasets, managed training, evaluation, serving, and application integration, while Custom Apps enables teams to create and publish workspace-specific interfaces for reviewing traces, experiments, annotations, and other agent data.

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