Rewind any execution. Watch it fail. Fix it.
When a workflow fails, the truth is in the execution — but raw logs bury it. Trace Replay reconstructs any run as a step-by-step timeline: what entered each node, what left it, how long it took, what it cost, and exactly where things went sideways.
The problems that pile up quietly.
Debugging a failed run means an hour of correlating timestamps across three different log systems.
"Works when I retry it" failures never get diagnosed because the original context is gone.
You can’t see what the model actually received — so prompt bugs hide behind template variables.
What changes for your team.
Minutes to root cause
The replay timeline shows the failing node, its exact input, and the error in context — the diagnosis that took an hour now takes minutes, not hours.
Ephemeral failures, preserved
Every execution is captured as it ran. The flaky 3am failure is sitting there in the morning, fully inspectable, even though the retry succeeded.
See what the model saw
Rendered prompts — after all templating and variable injection — are visible per call, so "why did it answer that?" has an inspectable answer.
Live in minutes, not sprints.
Capture executions
Node inputs, outputs, timings, token counts, and errors are recorded for every run, automatically.
Reconstruct the timeline
Each execution becomes a navigable replay: step forward and back through nodes, expand payloads, compare against a successful run.
Jump from anywhere
Every alert, incident, and recommendation links to the traces behind it — one click from "something’s wrong" to the evidence.
See it on your own AI stack.
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