CEO-Bench: Can Agents Play the Long Game?

2026-06-18 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Data Science & Analytics · Depth: Expert, extended

Summary

CEO-Bench introduces a novel benchmark designed to evaluate language model agents' capabilities in complex, long-horizon real-world scenarios by simulating a startup's operations over 500 days. Agents manage a fictional company through a programmable Python interface, accessing 34 tools and a 19-table business database to handle pricing, marketing, budgeting, and more. The environment features a partially observable, noisy, and evolving market with delayed and interconnected consequences, demanding strategic planning and adaptation. Initial evaluations reveal that most state-of-the-art models struggle, often leading to bankruptcy. Only Claude Opus 4.8 and GPT-5.5 managed to finish above the initial \$1M cash balance, yet neither consistently generated profit. The benchmark remains largely unsaturated, with an estimated upper bound of \$2.2B, indicating significant room for agent improvement.

Key takeaway

For AI Architects and Machine Learning Engineers designing or evaluating LLM agents for complex, long-horizon tasks, recognize that current models largely fail at sustained strategic control. Your development efforts should prioritize agents capable of integrating diverse skills, inferring hidden market conditions from noisy data, accurately forecasting delayed consequences, and continuously adapting strategies. This focus is crucial to move beyond isolated task execution towards agents that can effectively steer long-running operations through uncertainty.

Key insights

LLM agents excel at short tasks but struggle with sustained strategic control, long-horizon planning, and adaptation in complex, dynamic environments.

Principles

• Long-horizon agent evaluation needs dynamic, interconnected, partially observable environments.
• Complex agent success requires integrating diverse capabilities, not isolated task execution.
• Agent performance correlates with forecasting, hidden information inference, and adaptation.

Method

CEO-Bench simulates a 500-day startup operation via a Python API. Agents manage a company using 34 tools and a 19-table database, with success measured by final cash balance.

In practice

• Simulate customer cohorts to forecast future cash scenarios.
• Mine negotiation history to uncover hidden customer preferences.
• Prioritize targeted development for group-specific product improvements.

Topics

• Language Model Agents
• Long-Horizon Planning
• Agent Evaluation Benchmarks
• Startup Simulation
• Strategic Decision Making
• Adaptive AI

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.