Argo-Bench: A New Benchmark for Evaluating Data Agents in Enterprise-Scale Workflows

Summary:Argo-Bench is a novel evaluation framework designed to assess the performance of agents in real-world enterprise data science and analytics workflows. It simulates a large-scale food delivery platform in New York City with 81 million orders, 235 ERP tables, and 7.5 billion rows of data, incorporating factors like economics, fraud patterns, and marketplace incentives. Unlike traditional text-to-SQL benchmarks, Argo-Bench requires agents to reconstruct facts and take actions without direct access

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Argo-Bench: A Breakthrough in Enterprise-Grade Data Agent Evaluation

In enterprise-grade data science and analytics workflows, agents are required to process data across multiple tables, perform statistical analyses, and take actions based on the results. However, existing text-to-SQL benchmarks only evaluate query generation capabilities, and their answer keys are frequently found to be incorrect in audits. Due to the sensitivity of real enterprise data warehouses, these benchmarks are typically built on public datasets with single-table business events, failing to reflect the complexity of real-world scenarios.

Key Features of Argo-Bench

Argo-Bench addresses these issues with the following key features:

• Realistic Scenario Simulation: It simulates a large-scale food delivery platform in New York City using public data, peer-reviewed industry literature, and regulatory filings, with 81 million orders in 2024.
• Complex Data Environment: The simulation is exported to an ERP data warehouse modeled on the Oracle E-Business Suite schema, containing 235 tables and 7.5 billion rows of data, incorporating factors like economics, fraud patterns, and marketplace incentives.
• Agent Action Evaluation: Agents are required to take actions such as banning fraudulent accounts, allocating courier incentive budgets, or issuing back pay, and the grader scores each action based on its consequences in the simulator.
• Executable Reference Solutions: Each task comes with an executable reference solution that demonstrates solvability using only the warehouse.

Performance Test Results

In tests with 14 frontier and open-weight models, the strongest model scored 95 or higher on only 34.8% of the tasks, with an average score of 59.5. This indicates that current agents still face significant challenges in handling complex enterprise-grade data tasks.

Industry Impact and Future Directions

The release of Argo-Bench provides a new benchmark for evaluating agents' ability to understand and operate within real data environments. It aims to drive progress in agent technology, enabling more efficient and reliable performance in complex data science and analytics workflows. The research team hopes that Argo-Bench will inspire further innovation and ultimately lead to agents that can effectively handle the complexities of enterprise data environments.

Technical Highlights

1. Realistic Data Environment Simulation: Argo-Bench provides a more realistic evaluation environment than traditional benchmarks by simulating a complex ERP data warehouse.
2. Multi-Dimensional Evaluation: It evaluates not only query generation but also the actions and decision-making capabilities of agents.
3. Executable Reference Solutions: Providing executable reference solutions ensures the fairness and reproducibility of the evaluation.

Recommendations for Developers

• Focus on Agent Performance in Complex Data Environments: The release of Argo-Bench serves as a reminder that agents need stronger reasoning and decision-making capabilities to handle complex data tasks.
• Use Realistic Data Environments for Testing: It is recommended to use frameworks like Argo-Bench for testing during development to improve the reliability of agents in real-world applications.
• Explore New Agent Architectures and Algorithms: In light of the challenges revealed by Argo-Bench, explore new agent architectures and algorithms to enhance performance in complex data environments.

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Source: Hugging Face Daily Papers (2026-10-01)

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Tags: #Argo-Bench #Agent Evaluation #Data Science #ERP Data Warehouse #AI Benchmark