agent-based simulation

a platform for running large-scale agent simulations — populations of AI agents with defined behavior rules interacting in an environment. the core use case is social and economic modeling: what happens when you simulate a city, a market, an election, or an epidemic with agents that have heterogeneous goals and information? the value is generating synthetic data for hypotheses that can't be tested in the real world, or building intuition about emergent behavior.

the technical stack would need to handle massive parallelism efficiently — thousands to millions of agents running simultaneously. LLM-backed agents are increasingly feasible (see the Stanford "generative agents" paper), but expensive at scale; the interesting engineering problem is the right mix of lightweight rule-based agents and more expensive reasoning agents depending on the role they play. a platform-level abstraction would let researchers define agent schemas, environments, and interaction protocols without rebuilding the infrastructure. this connects naturally to the context window optimizer challenge — when each agent has its own context, managing what they know and remember becomes a core systems problem.