domains / families / devops / agent-observability

agent-observability

authentic

Instrument AI agents with tracing, token metrics, latency, and cost visibility. Use for reliability and debugging.

When to use

• **Autonomous AI agents** that make multi-step tool calls (e.g., coding agents, support agents, data-pipeline agents).
• **LLM-backed APIs** serving chat completions, summarisation, or classification behind a REST or gRPC gateway.
• **RAG pipelines** where a retriever fetches context from a vector store before prompting a model.
• **Multi-agent orchestrations** (crew-style or graph-based) where several agents collaborate on a single task.
• **Batch inference jobs** that process thousands of prompts against a model endpoint.
• You cannot answer "what is p95 latency for agent responses this week?"
• You have no per-request cost attribution.
• Debugging a bad agent response requires grepping raw application logs.

Skill page

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