I have been building a platform that uses agent-based simulation for market validation. The premise is simple: before you build a product, simulate a population of AI agents and run your go-to-market against them. Each agent decides whether to adopt, spread the word, or ignore you entirely.
The naive version of this is just prompting an LLM and asking "would you buy this product." That is useless. A real person testing an app brings frustration from their commute, impatience because they have 3 minutes before a meeting, confusion because they misread a button label. An LLM just answers coherently. That coherence is exactly what makes it a bad proxy.
So the system I built has three layers instead of one.
First, a world layer. Products, marketing channels, competition, and a social network with realistic topology. Information clusters, gets stuck, moves through bridges between communities. Not everyone knows everyone. If you want to model word of mouth you need a graph that actually behaves like one.
Second, an individual layer. Each agent has a backstory, personality traits, memory of past product experiences, and trust scores for people in their network. Agent 47 is a skeptic who needs three positive reviews before trying anything new. Agent 12 is impulsive and buys whatever their favorite creator mentions. These are persistent personalities, not random seeds.
Third, a neuron layer where LLMs do the reasoning. When an agent encounters a product they think about it in context. Is this relevant to me? Can I afford it? What have people I trust said about it? They change their minds. They get swayed by social proof. They experience something like buyer's remorse.
One simulation found that a 7-day free trial converted significantly worse than 14-day, but only in the skeptical user cluster. The reason was that skeptics needed more time to form a habit around the product, and 7 days was not enough. That is not a result you get from prompting an LLM once.
The signal is directional, not precise. Simulated humans are not real humans. But agent-based modeling has been used in epidemiology, urban planning, and economics for decades. Adding LLM-powered cognition to agents that already have memory and social structure is where it gets interesting.
Curious whether anyone else is working in this space or has thoughts on what it takes to make synthetic populations actually useful for something beyond benchmarks.
Site if you want to see it in action: siminsilico.com
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