Multi-agent reaction rounds

Multi-Agent Simulation AI

Simulate how stakeholders, customers, competitors, institutions, and audiences may react to the same event across multiple rounds before second-order effects surprise the team.

Create a multi-agent simulation See how it works

Built for scenario planning, not certainty. Every output should be reviewed against real evidence before an operating decision.

Scenario tape / illustrative

Multi-agent simulation for a high-stakes announcement

4 actors

Primary audienceFirst interpretation forms

Competitor or criticCounter-frame appears

Adjacent stakeholderSecond-order pressure grows

Dominant force

Actor incentives

Amplifier

Cross-actor influence

Evidence gap

Unknown reaction trigger

Input

Brief, evidence, actors, decision, event

Engine

Actor graph + multi-round interaction paths

Output

Second-order risks and response questions

What to bring

Bring the situation and the actors it may activate.

Multi-agent simulation works best when the source material identifies the event, the people or groups affected by it, the incentives in tension, and the evidence your team already trusts.

Scenario brief

The decision, announcement, launch, incident, policy, or market move you want to test before it meets the real world.

Actor and stakeholder context

Customers, competitors, employees, regulators, media, partners, communities, or fictional factions with different incentives.

Evidence and constraints

Research, notes, prior behavior, documents, public sources, timelines, known claims, and non-negotiable limits.

Where it earns its keep

See the reaction system, not just the first answer.

Many scenarios fail because teams reason from one actor at a time. MiroFish helps inspect how actors influence each other as the situation moves.

Stakeholder response01

Find who moves first and who reacts second.

Model how one group responds to the event, then how others reinterpret that response.

Decision rehearsal02

Pressure-test a decision before it creates public facts.

Simulate objections, incentives, escalation paths, and missing evidence before committing to a path.

Narrative risk03

Watch competing interpretations collide.

Explore how supporters, critics, competitors, and observers may pull the same evidence into different stories.

Multi-agent simulation needs interaction, not parallel summaries.

MiroFish builds a graph of actors, motives, constraints, and memory, then runs reaction rounds so cross-actor influence becomes visible.

Stage 1
Ground the scenario
Upload source material and define the decision, event, or message you want to test.
Stage 2
Build the actor graph
Map the people, groups, incentives, constraints, and memory that shape the reaction.
Stage 3
Run reaction rounds
Let the simulated actors respond over multiple rounds so the second-order path appears.
Stage 4
Question the report
Review the trajectory, risks, weak assumptions, and what evidence would change the conclusion.

What the report should answer

The report should make second-order effects inspectable.

Which actor or group responds first
Which incentive conflict drives the next round
Which message, claim, or action may be reinterpreted
Which evidence gap changes the scenario most
Which follow-up question should be tested next

Boundary conditions

Not a certainty machine.

Not guaranteed prediction of real-world behavior
Not useful without evidence about actors or constraints
Not a substitute for legal, financial, policy, or communications review
Not live monitoring unless current sources are provided

Why MiroFish

Multi-agent simulation AI should show interactions, not only opinions.

Generic AI often lists likely reactions. MiroFish gives the team an actor graph, reaction rounds, and an inspectable report.

Actors

Generic AI

Merged into one response

MiroFish

Modeled as distinct actors with incentives and memory

Time

Generic AI

One-shot summary

MiroFish

Multi-round reaction paths with second-order effects

Review

Generic AI

Advice without structure

MiroFish

Inspectable assumptions, gaps, and follow-up questions

FAQ

Multi-agent simulation AI, for scenarios with moving parts.

Use it when several actors may respond to one event and their reactions can influence each other.

What is multi-agent simulation AI?+

Multi-agent simulation AI models several actors or groups as distinct participants in a scenario, then explores how their reactions may influence each other over multiple rounds.

How is this different from asking a chatbot?+

A chatbot usually compresses the situation into one answer. MiroFish builds an actor graph, runs reaction rounds, and produces a report that exposes assumptions, conflicts, and second-order paths.

What should I upload?+

Upload a scenario brief, source documents, research notes, public articles, customer evidence, stakeholder context, or any material that defines the actors and decision.

Can it predict exactly what people will do?+

No. It is designed for scenario planning, not certainty. The value is in surfacing plausible paths, weak assumptions, and evidence gaps before a real decision is made.

Which teams should use it?+

Founders, strategy teams, product marketers, researchers, policy teams, communications teams, and operators can use it before launches, announcements, market moves, or sensitive decisions.

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