How Candidly Built State-Aware Agent Harnesses in LangSmith
Candidly 的代理 Cait 利用 LangSmith 标注管道,通过阅读部分追踪记录在对话中途推断用户状态并引导回复,该流程的人为一致性达到 92.3%。
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On August 26, 2026, the LangChain Blog announced two advancements: First, Candidly’s agent Cait used LangSmith for annotation pipelines to infer user states and guide responses by analyzing dialogue tracking records, achieving 92.3% human-like consistency in this process. Second, LangSmith introduced a technical engine called LangSmith Engine, designed to analyze large-scale tracking data, transform repetitive failures into actionable issues, identify operating patterns, and generate evaluations and repair solutions, thereby enhancing the agent’s own optimization capabilities.
LangSmith 推出了名为 LangSmith Engine 的技术引擎,旨在分析大规模追踪数据、将重复性故障转化为可操作问题并提出评估者与修复方案。该引擎通过深入分析运行轨迹,识别模式并生成具体的改进建议,从而提升智能体自身的优化能…
2 reportsCandidly 的代理 Cait 利用 LangSmith 标注管道,通过阅读部分追踪记录在对话中途推断用户状态并引导回复,该流程的人为一致性达到 92.3%。
LangSmith 推出了名为 LangSmith Engine 的技术引擎,旨在分析大规模追踪数据、将重复性故障转化为可操作问题并提出评估者与修复方案。该引擎通过深入分析运行轨迹,识别模式并生成具体的改进建议,从而提升智能体自身的优化能力。