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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。
Sergio Pino highlights DigitalOcean's new Simulations feature, part of its Agent Learning and Agent Data Services, which lets teams test agents against simulated production conversations, score interactions, and mock costly calls before deployment.
Claude Code now integrates with Open Telemetry, enabling tool calls to feed directly into existing trace pipelines for better agent observability using tools like Grafana and Jaeger.
Braintrust promotes its agent observability platform, offering a single, connected place for instrumentation, investigation, and measurement enhanced with intelligence.
The article introduces an open-sourced agent observability stack with four MIT-licensed repositories that focus on extracting and inspecting model beliefs to enhance AI agent debugging and performance.
This article introduces an agent observability and behavior tuning framework: adjusting agent behavior by promoting or demoting the resources it can access (tools, memory, subagents), and discusses lenses such as performance, execution, user alignment, and sources of evidence. From Alan Yahya of Lexifina.
Grafana Labs AI Week Day 4 focuses on AI agent trust and implementation, announcing the GA release of Agent Observability.
Oodle launches Agent Observability on Hacker News, offering agent traces at $10 per million spans to help AI-native teams detect silent failures and improve reliability.
A developer built a tray app that monitors AI coding agents operating on their machine and shares findings from the observations.
This tweet shares best practices for agent observability, covering metrics, logs, and traces to debug and optimize production AI agents.
A tweet highlighting Latitude, an open-source agent observability tool that helps visualize AI agent actions and token usage, with the ability to catch and fix recurring failures directly from the editor.
The author argues that current agent observability provides a trace of actions but lacks runtime justification for why actions were permitted, which is critical for production deployments involving money, data, or communications.
Arize Phoenix enables local-first, air-gapped observability for coding agents, allowing each agent to have its own traces, evals, and feedback loop for self-verification.
LangChain announced SmithDB, a distributed database for agent observability, Context Hub for managing agent context with an open memory standard, and Deep Agents v0.6 at Interrupt 2026, alongside enterprise case studies and keynotes by Andrew Ng and Harrison Chase.
LangChain announces SmithDB, a purpose-built distributed database for agent observability that powers LangSmith, offering improved performance and flexibility for complex agent trace data.
A developer tool that records AI agent runs inside a sandboxed GitHub repository, capturing terminal/browser sessions and turning them into replayable narrated videos for improved observability.
A developer built a real-time 3D visualization dashboard for monitoring AI agent working memory after losing $400+ to runaway agent loops, using color-coded nodes and edges to detect reasoning loops before they become costly. The post reflects on agent observability as an emerging category distinct from traditional microservice monitoring.