agent-observability

Trace agent decision-making, tool selection, and reasoning chains

Instrument safety checks, content filters, and guardrails for agent outputs

Strategies for evaluating agents in production - sampling, baselines, and regression detection

Track prompt versions, A/B test variants, and measure prompt performance

Instrument error handling, retries, fallbacks, and failure patterns

Instrument evaluation metrics, quality scores, and feedback loops

Instrument human approval workflows, feedback loops, and escalations

Plan what to measure in AI agent systems using tiered approach

Instrument LLM API calls with proper spans, tokens, and latency

Instrument RAG retrieval, memory operations, and context management

Instrument multi-agent workflows, handoffs, and parent-child relationships

Instrument sessions, conversations, and multi-turn interactions

Track token usage and costs across agents for budget management

Instrument agent tool executions with proper context and error handling