Trace Replay
Deterministically re-running an AI workflow from its stored trace — the debugging primitive that makes 'why did the agent do that' a question with an answer.
Production AI is not a prompt. It is a system of context, tools, permissions, traces, evals, and feedback loops.
What it is
Trace replay is the ability to take a workflow run from last Tuesday — with all its captured inputs, retrieved context, model outputs, and tool results — and re-execute it deterministically in dev. The developer can step through, change a prompt or model, and see how the outcome would have differed.
Why it matters
Agents are non-deterministic. Without replay, debugging a bad output is guesswork: you ask the user what they typed, you guess at what was retrieved, you can't reproduce. With replay, the bad run becomes a unit test.
How it works
Traces capture every input that could change behavior: event payload, retrieved chunks, tool inputs and outputs, model parameters and seeds where available, the model's response. Replay re-runs the workflow against captured inputs; differences from production are themselves a tracked signal. Tools like Langfuse, Arize Phoenix, and Helicone support this pattern.
Related resources
Trace-level visibility into every model call, retrieval, tool invocation, decision, approval, and failure inside an AI workflow — the substrate every other discipline (evals, optimization, governance) reads from.
The engine that runs an AI agent workflow as a durable, observable, restartable process instead of a one-shot script — what separates an agent demo from an agent deployment.
The test suite your AI workflows have to pass before any change reaches users — measuring quality, latency, cost, and safety on real production data instead of vibes.