Introducing: LangSmith Tuned Evaluators
LangSmith Tuned Evaluators provide an automated, cost-effective way to attach quality feedback (signals) directly to production traces and threads for AI agents. These out-of-the-box evaluators analyze agent interactions—such as identifying perceived errors or misunderstandings—and surface failure modes that traditional system error logging misses. LangChain manages the entire evaluation pipeline, including prompt writing, judge model management, and inference infrastructure, allowing teams to focus on agent improvement workflows.
Key takeaways
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Automated Quality Feedback
Tuned Evaluators automatically attach useful feedback signals to production traces and threads, helping identify agent behavior that needs attention (e.g., misunderstood user intent or contradictory answers).
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Perceived Error Detection
The initial evaluator, Perceived Error, analyzes multi-turn conversations to detect potential mistakes by the agent, even when no explicit system error occurs. This signal can be derived from subtle patterns like unresolved outcomes or user pivots.
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Turnkey Management
LangChain handles the entire evaluation lifecycle end-to-end: writing/testing prompts, managing judge models, benchmarking, and running inference infrastructure, eliminating the need for users to manage complex components. (See 0:28)
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Implementation Steps
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To use Tuned Evaluators, an organization admin must first enable the feature in LangSmith settings. After enabling, the evaluator can be attached to specific tracing projects.