AI Agents Fail Enterprise Reality Check

AI agents, while excelling at academic benchmarks, show significant limitations when applied to enterprise document-heavy workloads. Databricks research indicates that even top-performing agents achieve under 45% accuracy on tasks mirroring real business needs, revealing a disconnect between current AI capabilities and enterprise demands. This gap necessitates a shift in focus from abstract problem-solving to practical application.

To address this, Databricks introduced OfficeQA, a new benchmark designed to evaluate AI agents on grounded reasoning within complex proprietary datasets. Unlike existing benchmarks, OfficeQA uses real-world documents, such as U.S. Treasury Bulletins, to simulate economically valuable enterprise tasks. The benchmark's design focuses on challenges like parsing intricate tables, handling scanned documents, and performing multi-step analyses, exposing AI's current struggles with document complexity.

Testing revealed that AI agents face significant hurdles in parsing, document versioning, and visual reasoning. While pre-parsing documents improved accuracy, parsing remains a fundamental blocker. Furthermore, agents often fail to account for document revisions and struggle with interpreting charts and graphs. These findings serve as a critical reality check for enterprises, emphasizing the need to evaluate AI performance on actual business documents and plan for these persistent limitations.