AI

MiniCorp: The Last Mile of the AI Agent Firm

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 07/10/2026, 07:18:25 UTC.

Briefings

MiniCorp is an office simulator designed to study AI agents running enterprises by connecting external market dynamics with internal agent decision-making. It uses an e-commerce company as a demonstration to generate enterprise data at scale, recording communications and decisions to preserve information for replay under different scenarios. The system evaluates end-to-end fidelity against real market patterns to ensure agents learn realistic feedback rather than exploiting flaws.

Key points

• MiniCorp connects an external world modeling customers, competitors, and market mechanisms with an internal world of agents that observe events, discuss options, and make strategic decisions.
• The system records agent communications and decisions to preserve information for replay under different scenarios, enabling comparisons unavailable in static archives.

Context

This research addresses the scarcity of longitudinal enterprise data needed for AI agent training. MiniCorp provides a scalable environment for studying AI-run companies and offers a source of counterfactual data for evaluation.

Limitations

Evidence is limited to the e-commerce demonstration; no other cities, regions, or countries are supported. Results are based on experimental evaluations against real market patterns, not confirmed empirical studies. Specific agent names, company names, or exact dates are not provided in the source.

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