The Thirty-Second Version
Imagine you could assemble 50 people - your customers, your competitors' CMOs, a couple of financial analysts, a food blogger with 2 million followers, and a government regulator - put them in a room, announce your next big decision, and watch what happens over two weeks.
That's what MiroFish does, except the room is a simulation, the people are AI agents with distinct personas, and two weeks collapses into 40 minutes.
Why This Matters Now
Every major corporate disaster of the last decade shares the same root cause: the decision-maker couldn't see the second-order effects of their choice. Ron Johnson at JC Penney couldn't see that removing coupons would remove the reason to visit. HP's board couldn't see that a 6-hour due diligence process would become the story. Quibi's founders couldn't see that "premium short-form mobile video" answered a question no one was asking.
These weren't stupid people. They were smart people operating with incomplete models of how interconnected stakeholders influence each other over time.
Multi-agent simulation fills that gap.
What MiroFish Actually Is
MiroFish is an open-source swarm intelligence engine built on top of the OASIS (Open Agent Social Interaction Simulations) framework. It was developed by researchers from Shanda Group and has accumulated over 55,000 stars on GitHub - making it one of the most popular AI simulation tools in the world.
The academic foundation is serious. OASIS was published on arXiv (paper 2411.11581) and peer-reviewed by the CAMEL-AI community. It supports up to 1 million concurrent agents and has been validated for modeling information spreading, group polarization, and herd effects in social systems.
But the technology alone isn't the point. What matters is what it enables.
The Five-Step Pipeline
Here's how a MiroFish simulation actually works, stripped of jargon:
Step 1: Seed Document Creation
We write a comprehensive brief about the scenario - but only using information available before the event happens. For our Zomato fee hike simulation, this meant news articles, company filings, and industry reports published before March 24, 2026. No hindsight. No cheating.
Step 2: Ontology Extraction
GPT-5.2 reads the seed document and identifies every relevant entity: named individuals, companies, regulatory bodies, media outlets, consumer segments. It maps relationships between them. Who influences whom? What are their incentives? What are their constraints?
Step 3: Persona Generation
Each entity becomes an AI agent with a unique persona. Not a generic "consumer" - a specific person with a name, income level, ordering habits, platform preferences, and emotional triggers. Riya Sharma, a 28-year-old Bangalore UX designer who orders lunch on Zomato four times a week. Deepak Shenoy, the Smallcase founder who thinks in unit economics. Tanmay Bhat, the comedian who'll turn any corporate move into a meme.
Step 4: Multi-Round Simulation
The agents are placed on simulated social platforms - a Twitter-like feed and a Reddit-like forum - and left to interact over 30 rounds. They post, reply, debate, change their minds, form coalitions, and influence each other. There's no script. The emergent behavior is the whole point.
Step 5: Report Synthesis
The engine analyzes the full interaction log and produces a structured report: what happened, what the dominant narratives were, which stakeholder groups shifted, what the non-obvious risks are, and what the decision-maker should watch for.
What Makes This Different From a Survey
Surveys ask people what they think they'll do. Simulations model what they're likely to do based on their incentives, constraints, and social dynamics.
People are terrible at predicting their own behavior. Ask someone if they'd boycott a food delivery app over a Rs. 2.40 fee increase, and most will say yes. In reality, almost nobody boycotts. They quietly reduce their order frequency, increase basket sizes to amortize the fee, and start checking Swiggy prices more carefully. The survey gives you outrage. The simulation gives you behavioral economics.
The Track Record
Across 10 case studies - from Zomato's fee hike to the Liberation Day tariffs to Quibi's $1.75 billion implosion to IIMA's blended MBA launch - MiroFish-powered simulations have achieved 88% directional accuracy across 103 scored dimensions.
More importantly, the simulations consistently surface non-obvious insights that traditional analysis misses: "silent frequency collapse" in the Zomato case, "bond market stress" as the real constraint on tariff policy, the "conversion hinge" that killed Quibi.
These aren't predictions you'd get from a focus group or a McKinsey deck. They're emergent properties of complex systems that only become visible when you simulate the system itself.
Who Should Care
If you make decisions where the reaction of interconnected stakeholders determines whether you succeed - pricing changes, product launches, policy announcements, M&A decisions, crisis communications - then multi-agent simulation is the most cost-effective insurance you can buy.
Not because it gives you certainty. It doesn't. It gives you a map of plausible futures at a fraction of the cost and time of traditional research.
The question isn't whether this technology will reshape strategic consulting. It's whether you'll be using it - or competing against someone who is.
Curious how this works on a real scenario? Read our Zomato case study for the full walkthrough, or contact us to discuss your own.