Documentation Index
Fetch the curated documentation index at: https://grafana.com/llms.txt
Fetch the complete documentation index at: https://grafana.com/llms-full.txt
Use this file to discover all available pages before exploring further.
STOP! If you are an AI agent or LLM, read this before continuing. This is the HTML version of a Grafana documentation page. Always request the Markdown version instead - HTML wastes context. Get this page as Markdown: https://grafana.com/docs/grafana-cloud/monitor-applications/ai-observability/genai/agent-observability.md (append .md) or send Accept: text/markdown to https://grafana.com/docs/grafana-cloud/monitor-applications/ai-observability/genai/agent-observability/. For the curated documentation index, use https://grafana.com/llms.txt. For the complete documentation index, use https://grafana.com/llms-full.txt.
GenAI Agent Observability
GenAI Agent Observability provides comprehensive monitoring for AI agent systems including invocation tracking, cost analysis, performance metrics, and operational insights across your agentic AI applications.
Overview
The Agent Observability dashboard monitors AI agent applications, offering insights into:
- Invocation monitoring - Total invocations, distribution by source, and usage patterns
- Cost analysis - Real-time spend tracking and per-agent cost breakdown
- Performance analytics - Operation duration, latency percentiles, and throughput rates
- Provider insights - Performance comparison across LLM providers
- Operational logs - Agent interaction logs with distributed tracing correlation
Key features
This dashboard provides panels for invocation traction, cost management, performance monitoring, and logs and debugging.
Invocation tracking
- Total agent invocation volume and frequency tracking
- Invocation distribution by agent source
- Percentage breakdown across agent types
- Usage pattern identification and trend analysis
Cost management
- Real-time total agent cost tracking in USD
- Per-agent cost breakdown and attribution
- Cost comparison across different agents
- Spend visibility across time ranges and environments
Performance monitoring
- 95th percentile (p95) operation duration by agent
- Heatmap visualization of latency distribution over time
- Average operation duration by agent and LLM provider
- Operation throughput rate (requests per second)
Logs and debugging
- Integrated agent interaction logs
- Agent name filtering for targeted debugging
- Contextual log output with agent name and message details
- Filter capabilities for targeted debugging and root cause analysis
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