The CLARITY Act is Congress’s most significant attempt to define a market structure for digital assets in the United States. In this webinar, Messari breaks down the bill’s evolution across House and Senate drafts, the proposed frameworks for asset classification and regulatory oversight, and the key provisions shaping the debate. We also examine the political vote math, contested issues such as stablecoin rewards and DeFi compliance, and the most likely legislative outcomes. Join us for a clear, practical analysis of how the CLARITY Act could reshape crypto markets and institutional participation.

Register here to attend.

By: Alice Hou · @Fbitach · Research Analyst

Recently, a demo from a product called MiroFish stopped me cold.

MiroFish is a swarm intelligence engine: feed it real-world information, a breaking news event, a policy draft, financial signals, and it spawns thousands of autonomous agents, each with a distinct personality and behavioral logic, and lets them interact freely inside a simulated world. The output isn't a summary or a recommendation. It's an emergent social reality, shaped by the collision of synthetic minds, that evolves toward a future state.

In one demo, the project creator fed MiroFish the first 80 chapters of Dream of the Red Chamber, a War and Peace-comparable novel with hundreds of characters, sprawling family dynasties, and centuries of scholarly debate, and let the swarm play out the rest. The swarm had to synthesize the behavioral logic of an entire fictional society and evolve it forward into territory where even the source material has no answer. The output wasn't retrieved from any historical data. It was generated from the ground up, through social interaction between agents.

This is what makes swarms categorically different from prior generations of AI. Earlier agents were fundamentally backward-looking, drawing conclusions from existing data. Swarms generate genuinely new information through emergent interaction. They don't retrieve the future. They enact it.

That logic maps directly onto one of crypto's fastest-growing primitives.

The $20 Billion Bet on Human Intuition

Kalshi and Polymarket are each reportedly targeting $20 billion valuations, double where they stood just a year ago. The original thesis behind prediction markets was elegant: aggregate the dispersed knowledge of many humans into a single price, and you get something more accurate than any individual expert. The crowd, properly incentivized, knows best. But that thesis has always had a ceiling: you can only surface what participants already know or anticipate. The crowd can aggregate existing beliefs. It cannot simulate the interactions that produce new ones.

That is exactly what agent swarms do. When thousands of agents with distinct behavioral profiles interact inside an environment seeded with real-world data, they generate second and third-order consequences that human intuition routinely misses. The interactions themselves produce genuinely new information. More importantly, they expose a flaw in the "wisdom of crowds" framing that rarely gets acknowledged: human crowds are far more correlated than we admit. Shared media, similar professional frameworks, and common emotional responses to volatility all produce clustering that appears to be independent opinion but isn't. A well-designed agent swarm, with genuinely differentiated profiles and priors, can introduce more real diversity than the human crowd it supplements, pricing the long tail of questions that go underpriced today simply because not enough informed humans are paying attention.

Swarm intelligence doesn’t replace prediction markets. It makes them smarter: simulating the crowd before it forms, and becoming the oracle that human intuition alone never quite was.