What Is MiroFish? The Complete Guide to AI Prediction Simulation

MiroFish is an AI prediction platform that simulates how real-world scenarios play out. You describe a situation — a product launch, a policy change, a market event — and MiroFish runs a multi-agent simulation in which dozens of AI agents act as the stakeholders in your scenario: customers, competitors, journalists, regulators, investors. The result is a structured prediction report with a most-likely outcome, alternative scenarios with probabilities, key risks, and recommended actions.

What does MiroFish actually do?

Most AI chatbots answer questions from memory. MiroFish does something different: it stress-tests your scenario by letting simulated stakeholders react to it, argue about it, and influence each other — the way real markets and communities do.

A single prediction run moves through four stages:

• Seed Analysis — your prompt is expanded into a detailed scenario briefing: stakeholders, context, competing viewpoints.
• Graph Building — the engine maps the entities in your scenario and how they relate to each other in a knowledge graph.
• Multi-Agent Simulation — AI agents representing each stakeholder group interact over multiple rounds, reacting to the scenario and to each other. This approach builds on agent-based modeling, a technique used for decades in economics and epidemiology, now supercharged by large language models.
• Structured Report — the simulation is distilled into a readable prediction report with probabilities, risks, and next steps.

After the report is ready, you can chat with it — ask follow-up questions, probe the risks, or explore "what if" variations. If you want to understand the engine in more depth, read our step-by-step breakdown of how multi-agent simulation works.

What kinds of questions can it answer?

MiroFish handles any question you can phrase as a scenario:

• "How will the market react if we raise our SaaS prices by 20%?"
• "What happens to a mid-size retail brand that gets hit by a viral TikTok complaint?"
• "How would a new EU regulation on AI content labeling affect indie publishers?"
• "Will our product launch resonate with developers or get ignored?"

We collected 10 practical use cases with real prompt examples — from product launches to reputation management.

Why simulation beats asking a chatbot

Ask a chatbot "will my launch succeed?" and you get one model's single, averaged opinion. A simulation produces something richer:

• Emergent disagreement. Different agents want different things — early adopters get excited while budget-holders push back. Real markets are conflicts, not consensus.
• Second-order effects. A journalist agent picks up what customer agents are complaining about; a competitor agent responds to your pricing. Chains of reaction like these are where the surprises live.
• Probabilities, not vibes. The report commits to a most-likely outcome with an estimated likelihood, plus alternatives — so you can plan for more than one future. Learn how to read those numbers in how accurate are AI predictions.

Researchers at Stanford showed in the well-known Generative Agents paper that LLM-driven agents produce believable, emergent social behavior. MiroFish applies that idea to a practical question: *what happens next?*

How to run your first prediction

1. 1.Create an account — you get 1 free prediction credit, no card required, and nothing to install.
2. 2.Describe your scenario in the chat. Specifics beat generalities — our prompt-writing guide shows the 7 rules that make reports dramatically better.
3. 3.Watch the pipeline run — seed analysis, graph, simulation, report.
4. 4.Read the report and interrogate it — the follow-up chat is where the depth is.

The best way to understand MiroFish is to run one real prediction on a decision you're actually facing.

Ready to try? Run your first prediction free — or start with the broader picture in our guide to AI scenario prediction.