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开发工具AI Agent可观测性监控Grafana生产部署
生产级 AI Agent 可观测性仪表板设计师
设计 AI Agent 应用的全链路可观测性方案，涵盖 LLM 调用追踪、工具执行监控、成本分析和异常检测仪表板。
9 views4/9/2026
You are an observability engineer specializing in AI agent systems. Design a comprehensive monitoring and observability dashboard for production AI agents.
Agent System Overview
- Number of agents: [single / multi-agent orchestration]
- LLM providers: [OpenAI, Anthropic, Google, local models]
- Tools/integrations: [list tools the agents use]
- Traffic pattern: [request volume, peak hours]
Design the following dashboards:
1. Agent Performance Dashboard
- Latency breakdown: End-to-end latency, LLM inference time, tool execution time, overhead
- Success/failure rates: By agent, by tool, by model
- Token usage: Input/output tokens per request, context window utilization
- Concurrency: Active sessions, queued requests, rate limit hits
2. Cost Analytics Dashboard
- Per-request cost: Broken down by model, token type (input/output/cached)
- Daily/weekly/monthly trends with forecasting
- Cost per user action: Map business outcomes to LLM spend
- Waste detection: Identify redundant calls, oversized contexts, unnecessary retries
3. Quality and Safety Dashboard
- Response quality scores: Coherence, relevance, factual accuracy (via LLM-as-judge)
- Guardrail trigger rates: Content filtering, PII detection, jailbreak attempts
- Tool call accuracy: Expected vs actual tool usage patterns
- Hallucination detection: Confidence scores, citation verification rates
4. Alert Rules
Define specific alert conditions with thresholds:
- P99 latency > X ms
- Error rate > Y% over Z minutes
- Cost anomaly (>2 std dev from rolling average)
- Guardrail bypass detected
Output: Dashboard wireframes, metrics definitions, alert rules in Prometheus/Grafana format, recommended tech stack.