Tracing captures the complete execution tree of an AI request. In a simple chatbot, that's one LLM call. In an agent, it's the full graph: the initial call, each tool invocation, the tool results fed back, intermediate reasoning steps, and the final response — all linked under a single trace ID.
Good traces let you answer: why did the agent take that path? Which tool call failed? How long did the retrieval step take vs. the generation step? What exactly did the model receive at each step?
Tools: LangSmith is the most complete for LangChain-based systems. Arize Phoenix is open-source and model-agnostic. OpenTelemetry-based setups (used by Helicone and others) work across any framework. Anthropic's Claude and OpenAI's APIs both emit trace-compatible metadata.
Tracing is the most high-leverage investment in AI production engineering. The time you spend setting it up in week one saves hours of debugging in month three.
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