Technical Summary: CEO-BENCH

Problem Statement

Current language model (LM) agents demonstrate proficiency in isolated, short-horizon tasks such as software engineering, customer service, and web workflows. However, these tasks typically involve clear goals, immediate feedback, and short action sequences. Real-world challenges require a combination of sophisticated capabilities that remain largely untested: navigating long horizons under uncertainty, acquiring information from noisy environments, adapting to changing conditions, and orchestrating multiple interdependent decisions toward a coherent goal. Existing benchmarks (e.g., SWE-bench, WebArena) focus on short episodes with quickly observed outcomes, while others (e.g., Vending-Bench, Accounting-Bench) involve narrow decision sets and largely stable environments. There is a lack of evaluation frameworks that test an agent's ability to coordinate interdependent actions, infer hidden states from delayed feedback, and devise strategy amid non-stationary market conditions.

Methodology

The authors introduce CEO-BENCH, a benchmark that evaluates general long-horizon agent capabilities by simulating the operation of a startup over 500 simulated days.

Environment Design

• Simulation Scope: The agent manages a fictional subscription-software company ("NovaMind") starting with $1M in cash and zero customers. The simulation runs for 500 days (approx. 71 weeks).
• State and Dynamics: The environment is partially observable, noisy, and non-stationary.
  • Customers: 26 distinct customer groups, each with individual agents having hidden price and quality preferences. Customer behavior is governed by mechanistic rules (e.g., microeconomic participation rules) rather than opaque LLM judges.
  • Feedback Loops: Decisions have delayed and uncertain consequences. Revenue arrives on billing cycles, R&D takes time, and mistakes surface later through churn and reputation.
  • External Factors: The environment evolves due to macroeconomic cycles, competitor shocks (who adaptively raise quality expectations), and shifting customer preferences.
• Action Interface: Agents interact via a programmable Python interface (novamind_api) with access to:
  • 34 Tools: Covering pricing, growth, product quality, reliability, enterprise sales, information acquisition, and public communication.
  • 19-Table Database: A realistic SQL database containing ledgers, subscriptions, ad leads, support tickets, and social media feeds.
  • Workflow: Agents write and execute Python scripts to query the database, analyze data, and compose tools into custom workflows.

Experimental Setup

• Models Evaluated: The study evaluates a mix of closed-weight (e.g., GPT-5.5, Claude Opus 4.8/4.7, Gemini 3 Flash) and open-weight models (e.g., DeepSeek-V4-Pro, GLM-5.1).
• Protocol: Each model runs three 500-day simulations with a fixed random seed. Success is measured by the final cash on hand. Bankruptcy (cash < $0) terminates the simulation.
• Baselines: A rule-based heuristic baseline (no LLM calls) is included, which fixes prices and quotas while adjusting capacity based on usage.

Key Contributions

1. Novel Benchmark: CEO-BENCH is the first benchmark to evaluate agents in a realistic, large-scale startup simulation requiring the integration of coding, data analysis, and strategic thinking over long horizons.
2. Mechanistic Realism: Unlike benchmarks relying on LLMs as judges, CEO-BENCH uses explicit, mechanistic rules for customer behavior, market dynamics, and financial outcomes, ensuring that success depends on genuine strategic skills rather than exploiting brittle simulations.
3. Granular Action Space: The interface allows for fine-grained actions (e.g., targeting specific customer groups for ads or R&D), creating a combinatorially large space of strategies and failure modes.
4. Comprehensive Analysis: The paper provides both quantitative metrics (cash trajectory, survival days) and qualitative analysis of agent behaviors (memo content, code complexity, tool usage patterns).

Results

• Overall Performance: Most state-of-the-art models struggle to complete the simulation without bankruptcy. Only Claude Opus 4.8 and GPT-5.5 finished with a cash balance exceeding the $1M starting point in their best runs.
• Comparison to Baselines: The rule-based baseline achieved a final cash balance of $15.76M. While Claude Opus 4.8 ($27.8M) and GPT-5.5 (~$21.3M) surpassed this, they still fell far short of the estimated theoretical upper bound of ~$2.2B, indicating the task is far from saturated.
• Behavioral Patterns:
  • Exploration vs. Exploitation: Top-performing models (Opus 4.8, GPT-5.5) actively explored diverse strategies, simulated future scenarios, and adapted frequently. In contrast, weaker models often adopted passive "cash preservation" strategies or failed to coordinate growth with quality.
  • Sophisticated Analytics: Strong agents wrote code to simulate customer cohorts for cash forecasting (Opus 4.8) and mined negotiation history to infer hidden customer preferences (GPT-5.5).
  • Tool Usage: Successful models distributed actions more evenly across tools and allocated a higher percentage of development spend to targeted, group-specific improvements compared to other models.
• Ablation Studies:
  • Competitor Difficulty: Removing competitors or reducing their adaptability significantly lowered task difficulty, confirming that non-stationary market pressure is a critical challenge component.
  • Time Horizon: Even when the horizon was shortened to 50 days, most models failed to generate positive profits, suggesting current agents struggle with short-term orchestration as well.
  • Harness Sensitivity: Switching to standard agent harnesses (e.g., Claude Code, Codex) resulted in significantly fewer actions and inferior performance, highlighting the importance of the execution environment.

Significance and Claims

The paper claims that CEO-BENCH takes a "first step toward measuring the intelligence required to drive sustained, adaptive progress over time." It highlights a critical gap between the local tool competence of current models and the sustained strategic control required for real-world operation.

The authors assert that while agents can execute individual tasks, they fail to integrate these capabilities into coherent, adaptive behavior over extended horizons when faced with delayed feedback, hidden states, and changing conditions. The benchmark reveals substantial "headroom" for future models to improve in their ability to:

1. Infer hidden structure from noisy data.
2. Forecast delayed consequences.
3. Adapt to competitive pressure.
4. Coordinate interdependent decisions.

The paper concludes that to move beyond isolated task executors, the field needs evaluations that test whether agents can organize evolving systems toward distant goals, a capability CEO-BENCH is designed to measure.