Agents
Give this role a motive, information limit, and likely objection so the simulation can produce a reaction you can challenge.
Generative Agent Simulation
Generative Agent Simulation for evidence-led scenario rehearsal
Use MiroFish when language-model agents with memory and roles 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 agents and memory, interaction rounds, and structured reports for generative agent simulation.
Decision boundaryA generative agent simulation run is decision support, not a guaranteed prediction.
Useful inputThe first useful generative agent simulation run needs agents, memory, environment, timing, constraints, and the strongest contrary signal.
Scenario angle
The useful question is where language-model agents with memory and roles breaks.
For generative agent simulation, the page is worth its own route because the reader needs a bounded rehearsal around agents, memory, environment. Start by asking which role can change the story first, then keep that role visible through the report review.
Actor map
Separate the people and pressures before the first round.
Memory
Give this role a motive, information limit, and likely objection so the simulation can produce a reaction you can challenge.
Environment
Give this role a motive, information limit, and likely objection so the simulation can produce a reaction you can challenge.
Decision ledger
Read the report as a decision aid.
| Report item | Useful decision | Outside check |
|---|---|---|
| Memory pressure signal | Prepare the objection most likely to reshape language-model agents with memory and roles. | Look for fresh evidence from agents. |
| Weak assumption | Delay or revise the move if this assumption carries the plan. | check agents against environment before treating the branch as useful. |
| Branch comparison | Choose what to rerun with one changed condition. | Keep the changed condition visible in the next brief. |
Evidence choice
Pick source material that can be challenged later.
The first run should include agents, memory, environment, timing, constraints, and the strongest contrary signal. If a source is old, ambiguous, or politically loaded, mark it before running generative agent simulation so the report does not treat a weak claim as settled.
Validation plan
Turn output into real checks.
Interview
Ask agents whether the strongest assumption is real.
Evidence pull
Refresh facts that may have changed since the source packet was written.
Rerun
Change one assumption around language-model agents with memory and roles and compare the new branch map with the original.
Hard facts
Keep the operating limits visible.
WorkflowMiroFish uses seed material, graph context, simulated agents and memory, interaction rounds, and structured reports for generative agent simulation.
Decision boundaryA generative agent simulation run is decision support, not a guaranteed prediction.
Useful inputThe first useful generative agent simulation run needs agents, memory, environment, timing, constraints, and the strongest contrary signal.
Outside check
Leave with one verification move, not a pile of guesses.
The best next step after a generative agent simulation run is to check agents against environment before treating the branch as useful. That keeps the simulation useful without pretending it measured the real world.
Report preview
A useful report makes pressure points visible.
Reaction path
Which of agents, memory, environment moves first, which group amplifies the issue, and what evidence changes the path.
Assumption register
What the simulation inferred about language-model agents with memory and roles, what the source actually supports, and what remains unknown.
Follow-up question
The next prompt should change one condition, not restart the whole scenario.
Review owner
Name the person who can say the branch is weak.
Before using generative agent simulation output, assign one reviewer to challenge agents, one to challenge memory, and one to decide whether environment changes the next action.
FAQ
Generative Agent Simulation FAQ
What should I prepare for generative agent simulation?
Prepare the decision boundary, source notes, actor roles, known objections, timing, and the signals that would change the result.
Can generative 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