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LangSmith's update centers on tooling for production AI agents: an automated "Engine" for red teaming, issue detection, and fix validation, plus runtime and observability features. For developers, the practical question is whether automated red teaming and trace analysis catch real failures before users do, which vendor-reported figures like "60M traces analyzed" don't establish on their own. Independent benchmarks or case studies would show whether these gains hold outside the vendor's own ecosystem.
New in LangSmith: Engine v2, Managed Deep Agents, Fine-Tuning, and more
LangChain announced new updates to LangSmith. Updates include Engine v2 with red teaming and automatic testing, a new version of Managed Deep Agents, trajectories and more.
This week, hundreds of AI engineers joined us at Interrupt NYC to hear how teams are taking domain-specific agents to production and to discover the latest LangSmith announcements. Our latest releases focus on two pillars of the agent development lifecycle that help teams build with control and flexibility, compound the intelligence of their agents, and ensure strong governance: runtime and observability + evaluations. Runtime gives agents what they need to operate in production (such as memory, channels, tools and secure execution environments). Observability and evals help teams understand behavior, measure quality, and improve agents they more they are used. Sitting above is the intelligence layer, which works over the business logic, traces, evals…
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