Agent Observability

Monitor AI agent behavior with logs, traces, metrics, and cost telemetry. This skill covers the full observability stack for LLM-powered applications: from raw Prometheus counters to Grafana dashboards, OpenTelemetry tracing, structured logging, cost tracking, SLO definition, and PII redaction.

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When to Use

Apply this skill whenever you operate:

• 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.

Key signals that you need this skill:

1. You cannot answer "what is p95 latency for agent responses this week?"
2. You have no per-request cost attribution.
3. Debugging a bad agent response requires grepping raw application logs.
4. You have no alerting on token-usage spikes or elevated error rates.

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Core Metrics

Define these metrics at the application layer. All examples use the Prometheus client library naming conventions.

Latency

from prometheus_client import Histogram

# Total end-to-end latency for a full agent turn (user prompt -> final response)
AGENT_LATENCY = Histogram(
    "agent_request_duration_seconds",
    "End-to-end latency of an agent request",
    labelnames=["agent_name", "model", "status"],
    buckets=(0.25, 0.5, 1, 2, 5, 10, 30, 60, 120),
)

# Latency of a single LLM API call (one completion request)
LLM_CALL_LATENCY = Histogram(
    "llm_call_duration_seconds",
    "Latency of an individual LLM API call",
    labelnames=["model", "provider", "stream"],
    buckets=(0.1, 0.25, 0.5, 1, 2, 5, 10, 30),
)