Technical topic
AI agent observability
Tracing model calls, retrieval, tools, memory, delegation, state transitions, evaluations, and human review.
Direct answer
AI agent observability reconstructs the complete execution path from user intent through model, retrieval, memory, tool, workflow, delegation, approval, and verified outcome. The final answer is not enough: operators need stable identities, linked traces and events, privacy-aware evidence, cost and latency attribution, and explicit gaps when the system cannot observe what happened.
What this topic helps you decide
AI agent tracing
Preserve run, task, model, tool, retrieval, memory, delegation, and workflow relationships.
OpenTelemetry for AI agents
Join signals through stable identity while controlling sensitive data, cardinality, sampling, and retention.
Agent audit and debugging
Distinguish attempted actions, confirmed effects, evaluation results, and missing evidence.
Practical questions answered
- Why the final answer is not enough to observe an AI agent
A practical model for observing agent decisions, tool effects, evidence, cost, policy checks, and uncertainty instead of logging only the response text.
- What belongs in an AI agent trace schema?
A field-level design for tracing agent runs across models, retrieval, tools, policy decisions, outcomes, cost, and privacy boundaries.
- How should I trace model latency, streaming, and token usage?
A model-span profile that separates queueing, request time, time to first token, streaming, completion state, token usage, and semantic-convention version.
- How do I trace MCP and tool calls across a gateway?
A tool-action envelope that joins agent, MCP client, gateway, server, downstream service, and verified external effect without leaking payloads.
- How do I observe agent memory across sessions?
A memory-lineage schema for tracing proposals, admissions, versions, reads, promotion, expiry, conflict, deletion, and downstream effects.
- How do I trace multi-agent and durable workflows?
A workflow relationship map for delegation, messages, artifacts, waits, checkpoints, resumes, retries, effects, and asynchronous links beyond a span tree.
- How do I control sensitive data and cardinality in agent telemetry?
A telemetry privacy profile for data minimization, bounded attributes, content capture, hashing, sampling, access, retention, deletion, and incident exceptions.
Go deeper with a field guide
Observability for AI Agents
Tracing Models, Tools, Memory, and Multi-Agent Workflows with OpenTelemetry
Explore Observability for AI AgentsRelated books
Reusable resources
- Agent run envelope schema (JSON Schema)
- AI agent observability review (YAML)