'opentelemetry' 검색 결과

AgentCore の Observability ナレッジ。OpenTelemetry 必須4点セット（OTEL_EXPORTER設定・StrandsTelemetry・traceable デコレータ・Langfuse連携）、EMF メトリクス、Jaeger/コンソールエクスポーターでのトレース確認、トレースが出ない・繋がらない場合のデバッグ手順等

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Configures best-practice alerting policies for AI agents using OpenTelemetry (OTel) metrics, generating output as Terraform (.tf) configuration files. Use when analyzing, writing, or deploying alerting policies to monitor agent latency, error rates, token usage, and quality metrics. Don't use for standard infrastructure monitoring unrelated to AI agents, or when the agent is not instrumented with OpenTelemetry (for Reliability, Cost, Safety, Security alerts). NOTE: Reliability, Cost, Safety, and Security alerts use generic OTel metrics and work across runtimes (such as Cloud Run, Vertex AI). Quality alerts rely on Vertex AI Online Monitors and are strictly bound to Vertex AI deployments.

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Spring Boot 4 observability with Actuator, Micrometer, and OpenTelemetry. Use when configuring health indicators, health checks, custom metrics, distributed tracing, production endpoint exposure, Kubernetes probes, Cloud Run probes, logging, or monitoring. Covers Actuator endpoint security, Micrometer Timer/Counter/Gauge patterns, OpenTelemetry span customization, liveness/readiness probe setup, and metric tag best practices.

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Instrument applications with Azure Application Insights and OpenTelemetry for end-to-end observability. Use this skill when users ask about telemetry, tracing, metrics, logging, or monitoring setup. Covers SDK setup, correlation, sampling, dashboards, and alerts per OpenTelemetry standards.

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Azure Monitor OpenTelemetry Distro for Python. Use for one-line Application Insights setup with auto-instrumentation.

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Azure Monitor OpenTelemetry Distro for Python. Use for one-line Application Insights setup with auto-instrumentation.

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Azure Monitor OpenTelemetry Distro for Python. Use for one-line Application Insights setup with auto-instrumentation.

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Azure Monitor OpenTelemetry Distro for Python. Use for one-line Application Insights setup with auto-instrumentation.

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Azure Monitor OpenTelemetry Distro for Python. Use for one-line Application Insights setup with auto-instrumentation.

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Design layered protections for AI agents against malicious instructions, tool misuse, and data leakage. Provides guidance and code examples for input checks, isolated tool execution, permission limits, and audit logging to help review and strengthen agent security.

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• Review defenses against malicious instructions
• Isolate tool execution and restrict permissions
• Assess data leakage risks between tenants

Design LLM applications using patterns for answers grounded in documents, agents that use tools, and prompt testing. Implementation examples also cover monitoring response quality, latency, costs, and failures during operation.

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• Building an assistant that answers from internal documents
• Choosing an architecture for an agent that uses tools
• Comparing and versioning prompts

Add DeepEval tracing to Python AI applications so execution steps appear in Confident AI. Choose a supported integration or manual @observe instrumentation, then capture inputs, outputs, step types, tags, and metadata.

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• Trace execution steps in a Python AI application
• Select tracing integrations for an existing framework
• Add manual tracing with @observe

Build repeatable DeepEval tests for AI agents, chatbots, and applications that retrieve documents to answer questions. Reuse or generate evaluation datasets, inspect failures, and improve prompts, tools, or retrieval through repeated evaluation runs.

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• Add repeatable quality tests for an AI agent
• Evaluate chatbot behavior across multiple conversation turns
• Generate evaluation datasets from documentation

Set up application monitoring with structured logs, performance metrics, and traces that follow requests across services. Build dashboards and alerts, run load tests, investigate CPU and memory bottlenecks, and estimate future infrastructure capacity.

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• Set up application dashboards and alerts
• Investigate production issues using logs, metrics, and traces
• Run load tests before traffic increases

Explore and query measurements stored in Axiom MetricsDB. The skill discovers metrics and tags, checks measurement types and units, and builds MPL queries, Axiom’s metrics query language, to retrieve values and aggregate them over a chosen period.

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• Explore available metrics and tag values
• Check metrics for a specific service or host
• Aggregate values over a selected time range

Set up monitoring for deployed agents to inspect execution paths, latency, and model interactions. It explains Cloud Trace, prompt and response logging, BigQuery analytics, and external monitoring integrations, with checks for missing data and unwanted content capture.

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• Investigating agent latency and execution errors
• Configuring prompt and response logging
• Analyzing agent events in BigQuery

Use this skill when choosing or configuring NeMo Relay 0.6 or 0.7 observability through the built-in plugin, subscribers, or exporters, including raw ATOF events, ATIF trajectories, OpenTelemetry, OpenInference, or custom event handling.

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Unified APM and monitoring surface. Polls Datadog, New Relic, and OpenTelemetry backends for active alerts, error traces, and entity health. Use --watch for live polling every 60 seconds. Use --setup to configure monitoring credentials.

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Instrument browser/web apps with the Application Insights JavaScript SDK (@microsoft/applicationinsights-web). Use for Real User Monitoring (RUM) — page views, clicks, AJAX/fetch dependencies, exceptions, custom events, and browser-side GenAI agent traces correlated to backend OpenTelemetry traces. Covers SDK Loader Script and npm setup, framework extensions (React, React Native, Angular), Click Analytics, telemetry initializers, and OTel GenAI semantic conventions for agent/tool/model spans emitted from the browser.

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Monitoring and observability with OpenTelemetry, Prometheus, Grafana dashboards, and structured logging

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Expert performance engineer specializing in modern observability, application optimization, and scalable system performance. Masters OpenTelemetry, distributed tracing, load testing, multi-tier caching, Core Web Vitals, and performance monitoring. Handles end-to-end optimization, real user monitoring, and scalability patterns. Use PROACTIVELY for performance optimization, observability, or scalability challenges.

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Declarative OpenTelemetry-aligned telemetry vocabulary and instrumentation conventions for traces, metrics, logs, and PII handling

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Set up GitHub Copilot OpenTelemetry capture: configure the VS Code export settings, generate a local Grafana stack and dashboard, or generate the Azure collector, infrastructure, and dashboard for an organization.

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Create a shared ServiceDefaults project for Aspire applications. Centralizes OpenTelemetry, health checks, resilience, and service discovery configuration across all services.

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