Reproducibility in LLM Agent Simulations

Exact transcript matching is usually the wrong target for a stochastic system. A stronger target is procedural reproducibility: another reviewer can identify the evidence, prompts, agents, models, tools, and analysis rules used. Statistical reproducibility then asks whether repeated runs produce a comparable distribution of coded outcomes.

Document the intended decision and time horizon as part of the run. Without that context, later reviewers cannot tell whether a difference is meaningful. A changed phrase may be irrelevant; a changed recommendation or newly dominant failure mode is not.

At minimum, preserve source packet identifiers, extraction settings, prompt templates, agent profiles, relationship graph, model and provider identifiers, sampling parameters, tool definitions, interaction order, termination rules, and timestamps. Capture random seeds where the platform exposes them, but do not assume a seed guarantees identical output across provider updates.

Use immutable identifiers for decision-relevant runs. A human-readable label such as “launch test final” is not enough. Hashes, version-control references, exported configuration, and source snapshots make it possible to distinguish accidental edits from intentional variants.

Start with a pilot batch and code the outcomes. If the distribution is still changing materially or rare failures matter, add runs. A low-impact brainstorming exercise may need only a small set of diverse trajectories. A consequential decision with a close result needs more repetitions, stronger review, and possibly an independent method.

Report the count and uncertainty rather than presenting a generated frequency as a population probability. The run distribution describes the configured simulated system. Calibration against real observations is required before it can support claims about real-world rates.

Codes should represent mechanisms and outcomes the team can act on: objection type, coalition, adoption signal, escalation timing, misinformation path, intervention effect, or no-event result. Include an unknown category rather than forcing ambiguous runs into the preferred narrative.

Where judgment is required, have a second reviewer code a sample and resolve disagreements. Keep excluded runs visible with reasons such as tool failure, incomplete execution, or configuration error. Silent exclusions can make unstable systems look consistent.

Hosted models can change behavior even when a familiar product name remains. Preserve the most specific model identifier available and the run date. Maintain a small benchmark suite containing common, adversarial, and edge-case scenarios so updates can be evaluated consistently.

Do not merge runs from materially different models into one distribution unless the comparison is intentional. Report whether drift affects wording, mechanism, frequency, or the recommended action. The last category deserves the strongest review.

Sensitive source material may prevent public release, but an internal reviewer still needs access under appropriate controls. Separate the public methodology from restricted evidence and state what cannot be independently inspected.

A concise record is better than an undocumented archive. Link the manifest to the decision memo, preserve artifacts for the required retention period, and name an owner who can explain the run after the original team has moved on.