Agent Observability & Monitoring
PROImplement comprehensive observability for AI agents with distributed tracing, metrics collection, logging, and alerting using OpenTelemetry and modern monitoring stacks.
Monitor AI agents in production. Logging, metrics, alerting, and debugging patterns for autonomous systems.
Example Usage
“Design an observability system for our multi-agent customer support platform. Track every agent interaction, tool call latency, LLM token usage, and task completion rates. Set up alerts for high error rates, slow responses, and unusual patterns. Include dashboards showing agent performance, cost tracking, and user satisfaction correlation.”
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Suggested Customization
| Description | Default | Your Value |
|---|---|---|
| Monitoring infrastructure | opentelemetry | |
| Metrics storage backend | prometheus | |
| Distributed tracing backend | jaeger | |
| Log aggregation system | elasticsearch |
What You’ll Get
- OpenTelemetry tracing setup
- Prometheus metrics configuration
- Structured logging implementation
- Grafana dashboard definitions
- Alert rule configurations
- LLM cost tracking
- Quality evaluation metrics
Research Sources
This skill was built using research from these authoritative sources:
- McKinsey: The State of AI 2025 Enterprise AI agent adoption and monitoring trends
- OpenTelemetry Generative AI Semantic Conventions Standard conventions for AI/LLM observability
- IBM: AI Agents 2025 Expectations vs Reality Enterprise AI agent deployment challenges and monitoring
- Deloitte: Agentic AI Strategy Enterprise agentic AI strategy and governance
- PwC AI Agent Survey AI agent adoption trends and enterprise concerns
- AI Agent Statistics 2025 Market data on AI agent adoption and performance metrics