Agent tracing
Agent tracing captures the full execution graph of an agent run — every step, every tool call, every model output — so engineers can debug, audit, and improve the agent over time.
Production agents in 2026 are impossible to debug without tracing. Each agent run emits a structured trace: planner step, tool calls + arguments + results, model outputs at each step, latency per stage, total token spend, final outcome. Tools that ship tracing: Langfuse, Braintrust, Phoenix (Arize), LangSmith, OpenLLMetry. Best practice: trace 100% of production runs but sample for evals; pin prompt version IDs to traces; alert when failure rate or token budget spikes. Tracing turns agents from black boxes into observable systems.
When to use agent tracing
- Any production agent.
- Multi-step LLM pipelines.
Common mistakes
- Tracing only failures — you need success samples to know what good looks like.
- No version pinning in traces — can't attribute regressions to prompt changes.
FAQ
What is agent tracing?
Agent tracing captures the full execution graph of an agent run — every step, every tool call, every model output — so engineers can debug, audit, and improve the agent over time.
When should I use agent tracing?
Any production agent. Multi-step LLM pipelines.
What are the most common mistakes with agent tracing?
Tracing only failures — you need success samples to know what good looks like. No version pinning in traces — can't attribute regressions to prompt changes.
Related terms
- Evals (LLM evaluations) — Evals are systematic tests that measure how well a language model or LLM-powered system performs on a defined task using a golden set of inputs and reference outputs.
- Prompt versioning — Prompt versioning is the discipline of treating prompts as source-controlled artefacts — each prompt has a versioned ID, a deploy history, and a regression-tested change log.
- AI agent — An AI agent is a system where a language model autonomously plans and executes a sequence of tool calls to accomplish a goal.
- Agent loop — An agent loop is the repeating cycle of an AI agent — observe state, decide on an action (usually a tool call), execute, observe the result, and repeat — until a goal is reached or a stop condition fires.
Last updated: 2026-06-01. Raw markdown: https://promtable.com/glossary/agent-tracing.md.