OpenEnv-SEC: An Agent Benchmark Environment for Financial Analysts' Workflows

This article introduces OpenEnv-SEC, an open benchmark environment designed specifically for training and evaluating AI agents' performance in the real-world workflows of financial analysts. It aims to address the problem that existing benchmarks struggle to assess agents' comprehensive capabilities, simulate real financial analysis scenarios, provide a multi-dimensional evaluation system, and support the research, development, and application of financial AI.

The daily work of securities analysts involves multiple links such as monitoring announcements, analyzing financial reports, comparing industries, and building valuation models, which have complex dependencies. It requires agents to have long-term memory, planning, and tool usage capabilities, with extremely high accuracy requirements. Most existing mainstream benchmarks focus on single capability dimensions (e.g., MMLU for knowledge testing, GSM8K for math testing), making it difficult to evaluate comprehensive performance in real scenarios. Existing tests in the financial field remain at the level of simple Q&A and cannot simulate challenges such as integrating massive unstructured data and time-sensitive decision-making.

This benchmark adopts a modular design, with core components including a task definition layer (breaking down atomic subtasks), a data supply layer (providing structured financial reports, unstructured news, and simulated market data), a tool interface layer (supporting tool calls such as database queries and search engines), and an evaluation index layer (multi-dimensional scoring system). Its features include simulating real work constraints: tasks have time limits, incomplete information requires active search, open answers require reasonable reasoning, and noise interference is introduced, forcing agents to demonstrate real understanding and judgment capabilities.

This benchmark evaluates agent performance from five dimensions: information retrieval capability (efficiently locating relevant information), data analysis capability (accuracy in numerical calculation, trend identification, and anomaly detection), reasoning and planning capability (formulating reasonable analysis strategies and execution sequences), tool usage capability (proper tool invocation and parameter setting), and report generation capability (clear structure, accurate terminology, and logically rigorous conclusions).

For developers: It provides a capability map to help identify weak links (e.g., optimizing strategies for time-consuming searches, strengthening verification for calculation errors); for financial institutions: It objectively evaluates the boundary of AI capabilities to avoid improper use; for regulators: It provides technical references for the supervision of financial AI applications. Compared with benchmarks in other fields such as WebShop, OpenEnv-SEC has financial characteristics: mixed structured and unstructured data, emphasis on accuracy and interpretability, probabilistic answers requiring confidence assessment, etc.

Future plans include expanding coverage to financial sub-fields such as fixed income analysis and derivative pricing, introducing multi-agent collaboration scenarios and real-time data stream processing tasks, and developing more refined human alignment evaluations. OpenEnv-SEC represents an attempt to evolve AI evaluation toward complex real scenarios, providing a solid infrastructure for the development of financial AI and promoting agents from the laboratory to actual production environments.