agent-observability

# 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. --- ## Core Metrics Define these metrics at the application layer. All examples use the Prometheus client library naming conventions. ### Latency ```python 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), ) # Latency of tool/function calls executed by the agent TOOL_CALL_LATENCY = Histogram( "agent_tool_call_duration_seconds", "Latency of a tool call executed by the agent", labelnames=["tool_name", "agent_name", "status"], buckets=(0.05, 0.1, 0.25, 0.5, 1, 2, 5, 10), ) ``` ### Token Usage ```python from prometheus_client import Counter, Histogram PROMPT_TOKENS = Counter( "llm_prompt_tokens_tota...