FounderBench: Evaluating LLM Agents on Sequential Startup Decisions

Existing agent benchmarks emphasize tool use, web navigation, and software editing, but rarely isolate repeated operating decisions under scarce resources and delayed consequences. We introduce FounderBench, a controlled benchmark for startup-like sequential decision making. Given a simulated company state and noisy market information, an agent must choose from 13 structured business actions over a fixed horizon; a deterministic simulator updates cash, demand, quality, and risk, and task-specific code maps final outcomes to a 0–100 score. Free-form rationale is logged for audit but never scored. The suite contains 50 synthetic public tasks balanced across 10 operating families. Headline gap: a task-aware calibration policy averages 80.90 (37/50 solved), while the best hosted single run reaches only 67.69 (32/50); no human-founder baseline is reported. Rankings reverse between average score and solved count, and family profiles expose distinct operating failures. We release the suite, simulator and scoring code, adapters, validated task-level results, and regeneration scripts at https://github.com/louiswang524/FounderBench. All hosted rows are single runs on visible public tasks; scores do not establish real-world startup competence.