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作者与 Antaripa Saha 合作发布了一篇关于 AI 代理调试的深度教程，讲解如何阅读代理 trace、定位失败原因，并将失败案例转化为 evals 以提升生产环境可靠性。
Arize is donating OpenInference to OpenTelemetry. Dynatrace’s Observation Deck welcomes Arize’s Mickey King and OTel semantic conventions maintainer Ludmila Molkova to discuss what this donation means for GenAI observability’s move toward vendor-neutral standards.
作者探索了 OpenTelemetry 与 AI 代理的结合，基于 OpenAI Agents SDK 构建了一个 NBA 演示应用并接入 OTel 进行追踪和指标采集，同时讨论了当前标准仍在发展中带来的取舍（如 guardrails 尚未被观测支持）。
Arize Phoenix evals (TS) now supports running any classification evaluator on a decision model (e.g. gpt-6-luna via OpenAI's Decisions API), returning labels and probabilities per option with each call traced as a decision span.
neatroots is an open-source, local-first app for developers building AI agents, offering trace collection, run checks, multiple run views, and replay controls to debug and improve agent runs. It integrates with coding agents via MCP tools and supports OpenTelemetry and various trace inputs.
Anthropic 宣布 Claude Platform 的 Managed Agents、Messages API 和 Claude SDKs 支持发送 OpenTelemetry 追踪数据到 Arize Phoenix、Grafana Cloud、Datadog 等任意 OTel 观测工具，目前处于公测阶段。
Claude Platform traces can now be exported to LangSmith via OpenTelemetry, letting users inspect every step of Claude Managed Agents sessions and Messages API requests. Available in public beta with multiple export paths including server-side destinations, Claude SDKs, and the LangSmith SDK wrapper.
NestJS Observe 推出零配置可观测性方案，由 NestJS 创始人打造，只需添加 SDK 和密钥即可自动追踪控制器、Provider、数据库查询、错误、任务、日志与运行时指标，每月 30 万事件内免费。
用户转发并评论 LangChain 在 Interrupt 大会上宣布的 LangSmith AI Search 功能，认为用自然语言搜索 trace 能大幅节省团队排查 agent 故障的时间。
culpert 是一个实验性的 Rust 堆分配分析器，能将采样到的分配归属到 span，导出 pprof 兼容报告并在 CI 中检测分配回归，由 Cloudflare 开发并已在生产前捕获多个内存泄漏问题。
A talk shared by LangChain: every agent builder should be able to answer three questions—where the agent fails, how to reliably reproduce the failure, and how to prevent it from happening again. The talk introduces a systematic approach to improving agents through production tracing, specialized judge models, and a "world spec" simulation environment.
OpenTelemetry 官方博客介绍了如何从产品设计之初就实现 OTel 原生支持，让用户可以将日志、追踪和指标导出到任意可观测性后端，避免供应商锁定。文章概述了良好的遥测系统应具备的特性，包括符合语义约定、保留丰富上下文以及通过 OTLP 协议实现标准化导出。
Arize's OSS team shares how they benchmark AI coding agents (PXI, Claude Code, Codex) using Harbor sandboxes and Arize Phoenix tracing, showing that even successful agents reveal very different tool calls, latency, and efficiency patterns.
LangChain introduces an Engine that learns how your agent works by reading traces and repositories, then tests for agent-specific weaknesses such as hallucinations and system prompt violations, returning a list of verified issues to fix before they impact users.
The author discusses common challenges in debugging AI agent evaluation failures, seeking insights on efficient investigation methods and the reliability of comparison runs versus other evidence sources.
The article discusses common errors in using AI agent traces and offers advice on proper implementation.
LangChain introduces Trajectories in LangSmith, a chronological view of agent sessions that simplifies debugging by aggregating messages from humans, AI, and tools in order for easier navigation and analysis.
The article explores common challenges in running multiple AI agents, such as debugging, state management, and cost, and invites community input on real-world experiences in production.
Enzo Health uses LangSmith to trace and evaluate its clinical AI pipelines in a HIPAA-compliant healthcare setting, and the company is hiring engineers to advance AI in clinical workflows.
Clay demonstrates integrating LangSmith tracing into their agent harness built on Vercel AI SDK, requiring only one line of code.