What should I prepare for llm agent simulation?
Prepare the decision boundary, source notes, actor roles, known objections, timing, and the signals that would change the result.
LLM Agent Simulation
LLM Agent Simulation for evidence-led scenario rehearsal
Use MiroFish when LLM actors inside a bounded scenario needs more than a one-shot answer. MiroFish uses seed material, named actors, reaction rounds, and structured reports; treat every run as decision support, not a guaranteed prediction.
Operating facts
Keep the limits visible before opening the console.
WorkflowMiroFish uses seed material, graph context, simulated llm actors and rules, interaction rounds, and structured reports for llm agent simulation.
Decision boundaryA llm agent simulation run is decision support, not a guaranteed prediction.
Useful inputThe first useful llm agent simulation run needs llm actors, rules, round output, timing, constraints, and the strongest contrary signal.
Scenario angle
The useful question is where LLM actors inside a bounded scenario breaks.
For llm agent simulation, the page is worth its own route because the reader needs a bounded rehearsal around llm actors, rules, round output. Start by asking which role can change the story first, then keep that role visible through the report review.
Boundary
This is rehearsal, not measurement.
LLM Agent Simulation in MiroFish can expose plausible reactions and research questions, but it cannot replace recruited participants, live market behavior, expert review, legal review, medical advice, financial advice, or accountable judgment.
Source packet
Bring the material that makes the run inspectable.
Start with llm actors, rules, round output, timing, constraints, and the strongest contrary signal. MiroFish works better when each claim can be traced back to a source or an explicit assumption, especially when the run is about LLM actors inside a bounded scenario.
Good packet
LLM actors inside a bounded scenario; one time horizon; named roles; known constraints; and at least three signals to review after the first report.
Evidence choice
Pick source material that can be challenged later.
The first run should include llm actors, rules, round output, timing, constraints, and the strongest contrary signal. If a source is old, ambiguous, or politically loaded, mark it before running llm agent simulation so the report does not treat a weak claim as settled.
Workflow
Move from brief to review in four deliberate steps.
Frame LLMFrame LLM actors inside a bounded scenario with one decision boundary.
Build anBuild an actor graph for LLM actors, Rules, Round output.
Run reactionRun reaction rounds and watch for rules changing the interpretation of LLM actors inside a bounded scenario.
Question theQuestion the report and check llm actors against round output before treating the branch as useful.
Why MiroFish
Use a structured world instead of a loose answer.
| Need | General chat | MiroFish |
|---|---|---|
| LLM actors behavior | One compressed explanation. | Named roles with incentives and memory. |
| Second-order effects | Often summarized too early. | Reaction rounds make rules changing the interpretation of LLM actors inside a bounded scenario inspectable. |
| Review | Hard to trace after the answer. | Report, assumptions, and follow-up questions stay visible. |
Outside check
Leave with one verification move, not a pile of guesses.
The best next step after a llm agent simulation run is to check llm actors against round output before treating the branch as useful. That keeps the simulation useful without pretending it measured the real world.
Signals
Decide what would change the next run.
| Signal | Why it matters | Next action |
|---|---|---|
| LLM actors repeats the same objection | The issue may be structural rather than wording. | Strengthen proof or change the decision. |
| Rules reacts after one source changes | The path depends on a volatile fact. | Refresh the source before using the result. |
| Round output blocks the path | The rollout may need sequencing. | Run a narrower check around LLM actors inside a bounded scenario. |
Review owner
Name the person who can say the branch is weak.
Before using llm agent simulation output, assign one reviewer to challenge llm actors, one to challenge rules, and one to decide whether round output changes the next action.
FAQ
LLM Agent Simulation FAQ
Can llm agent simulation replace real evidence?
No. Use it to generate hypotheses, pressure points, and validation questions, then confirm important claims with real data or accountable review.
What does MiroFish return?
A structured report with reaction paths, weak assumptions, evidence gaps, and follow-up questions you can challenge.
When should I rerun it?
Rerun after changing one important assumption, such as the actor list, timing window, evidence strength, or public message.
Next paths