MiniCorp: The Last Mile of the AI Agent Firm
MiniCorp simulates an AI-run e-commerce firm that generates longitudinal and counterfactual enterprise training data.
MiniCorp is an office simulator for studying agents that collectively run a company and for generating longitudinal enterprise data that real archives rarely provide. An e-commerce demonstration links an external world of customers, competitors, and market mechanisms with internal agents that observe events, discuss options, and make decisions whose market effects inform later choices. Checkpointing replays the same situation under alternative decisions. Evaluations against empirical market patterns aim to limit simulator exploits, and experiments show cross-role coordination, adaptation to feedback, and continued advertising exploration under explicit long-term guidance.
- An external market and internal agent firm interact with lasting feedback.
- Records preserve information available at decision time and later outcomes.
- Checkpoint replay supplies counterfactuals missing from static archives.
- Agents coordinate roles and sustain advertising exploration despite weak early returns.
From the original article
1 min read at huggingface.co
The last mile toward enterprise AGI is a company that runs itself. Training and adapting such agents require longitudinal enterprise data, which remain scarce, costly to acquire, and often restricted by privacy constraints. Historical archives are also frequently incomplete and record only what actually happened.
By Jingying Zeng, Zhenwei Dai, Jinning Li · huggingface.co
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