1. Ground the world
Add a brief, notes, market context, transcripts, or other seed material so the simulation starts from the specific situation instead of a generic prompt.
Multi-agent simulation
Multi-agent simulation for questions where one AI answer is not enough.
MiroFish helps teams rehearse scenarios with multiple simulated actors. Bring seed data, define the decision, and inspect how agents with different incentives may react, disagree, and shift the outcome.
When multi-agent simulation helps
A single chatbot response can be useful for summarizing facts, but many planning questions depend on interaction. A product launch, policy change, market narrative, community decision, or investor update can trigger different reactions from different groups.
Multi-agent simulation gives those groups separate perspectives inside one run. The value is not a magic prediction. The value is seeing where the scenario is stable, where it is fragile, and which assumptions deserve a second test.
How a MiroFish simulation is structured
2. Create the actors
MiroFish organizes entities, incentives, and relationships, then prepares simulated agents that can represent stakeholders or viewpoints.
3. Review the report
The final report should summarize reactions, tensions, scenario branches, assumptions, and follow-up questions for another run.
Example: launching a new AI product
A team can paste a launch memo, audience notes, competitor context, pricing assumptions, and a list of concerns. MiroFish can simulate how buyers, skeptical users, partners, competitors, and internal reviewers react to the same announcement.
The report can then show which objection appears first, which group changes position, and which message makes the plan easier to understand. A second run can change one variable, such as price or launch timing, so the team can compare outcomes.
What to do with the output
Read the simulation as a structured rehearsal. The best use is to find disagreement, pressure-test a plan, and decide what evidence to collect next. If the report gives one dominant outcome, ask what would make that outcome weaker. If the report shows several branches, compare which branch is easiest to test with fresh data.
For high-stakes decisions, combine the MiroFish report with measurement, domain expertise, and human review. Multi-agent simulation is strongest when it makes uncertainty more visible.