Argo-Bench Tests Data Agents on Enterprise-Scale Workflows

Introduction

Enterprise data work rarely ends when a model produces a syntactically valid SQL query. Analysts and operators must locate information across disconnected domains, reconstruct the relevant business facts, run statistical analyses, and then decide what the company should do. Argo-Bench is designed to evaluate that complete loop rather than query generation in isolation.

Beyond text-to-SQL

Established text-to-SQL benchmarks generally measure whether a natural-language request can be converted into a query. The paper notes that audits have also found frequent errors in some benchmark answer keys. More fundamentally, a correct query is only an intermediate step in a real enterprise workflow. A decision may depend on dozens of tables, ambiguous business definitions, and the expected consequences of an intervention.

Argo-Bench builds a simulated food-delivery business in New York City using public data, peer-reviewed industry literature, and regulatory filings. The environment represents 81 million orders in 2024 and exports the simulated world into an ERP warehouse modeled on Oracle E-Business Suite. The warehouse contains 235 tables and 7.5 billion rows. It also incorporates grounded economics, fraud patterns, and marketplace incentives.

The simulator’s ground-truth state is withheld from the agent. Instead, the agent must navigate the warehouse and infer the facts needed for each task. It can then file operational actions, including banning fraudulent accounts, allocating courier incentive budgets, or issuing back pay. The grader evaluates those actions by their consequences in the simulator, not merely by checking whether the agent’s SQL looks plausible.

Key points

• End-to-end evaluation: Tasks cover data science and analytics workflows rather than query writing alone.
• Large-scale schema navigation: Agents must identify relevant tables, interpret relationships, and reconstruct hidden facts.
• Outcome-based grading: Actions are scored according to their effects in the simulated business environment.
• Executable reference solutions: Each task includes a reference solution showing that it can be solved using only the warehouse.
• A substantial performance gap remains: The best of 14 frontier and open-weight models reached a score of 95 or higher on just 34.8% of tasks.

Why it matters

Argo-Bench highlights the gap between retrieving data and making a reliable business decision. A model may produce a valid query yet still miss a critical table, misunderstand a metric, or choose an intervention without accounting for side effects. Evaluating the final action makes those failures visible and better reflects what companies ultimately care about.

The benchmark also points to a broader capability stack for data agents: schema understanding, statistical reasoning, planning, verification, and awareness of business consequences. Its simulated setting cannot represent every industry or every problem found in production warehouses, but it provides a controlled way to test whether an agent can close the loop from evidence to action. The early results suggest that this remains considerably harder than generating an apparently correct SQL statement.

Source: Hugging Face Daily Papers