Even Top AI Agents Struggle with Real‑World Enterprise Docs — OfficeQA Exposes a Major Gap
Enterprises have been banking on the promise of AI: that large language models (LLMs) and AI agents can not only ace abstract reasoning tasks, but also shoulder the everyday grunt work — parsing reports, extracting numbers, and surfacing actionable insight from messy corporate documents. But a new benchmark from Databricks shows that reality is far harsher.
The Benchmark: From Olympiads to Real‑World Complexity
Most widely used AI benchmarks — tasks like abstract math, puzzles, or PhD‑level questions — test skills like reasoning, memory, or abstract thinking. But in real enterprise settings, the work is rarely abstract. Instead it involves digging through thousands of pages of documents, tangled tables, PDFs, scanned images, and historical reports. ([Venturebeat][1])
To bridge this divide, Databricks launched OfficeQA — a benchmark designed to reflect the kinds of document‑heavy, data‑driven tasks enterprises actually face. The corpus for OfficeQA is drawn from decades of U.S. Treasury Bulletins: roughly 89,000 pages spanning 80+ years, including scanned reports, tables with nested headers, charts, and cross‑report dependencies. ([Venturebeat][1])
OfficeQA comprises 246 questions (split into “easy” and “hard” based on model performance), each requiring careful retrieval, numeric computation, or cross‑document analysis. Answers are deterministic — numbers, dates, or small lists — enabling automated evaluation without human judging. ([Databricks][2])
How Leading AI Agents Fared — The Results Are Grim
Databricks evaluated two leading agent configurations on OfficeQA: a Claude Opus 4.5 Agent (via Anthropic) and a GPT‑5.1 Agent (via OpenAI’s File Search & Retrieval API). ([Venturebeat][1])
- Raw PDFs: Claude scored only 37.4% accuracy; GPT‑5.1 reached 43.5%. ([Venturebeat][1])
- With parsed documents (using Databricks’ ai_parse_document tool): Claude’s accuracy surged to 67.8%, GPT‑5.1 to 52.8%. ([Venturebeat][1])
Even with the parsing boost, both agents stayed well below perfect — and performance on the hardest subset remained disappointing. ([Venturebeat][1])
The Core Problem: It’s Not Reasoning — It’s Parsing
The OfficeQA authors conclude that parsing, not reasoning, is the main bottleneck. Modern LLMs handle abstract thought well — but struggle mightily when faced with messy real‑world documents:
- Complex tables with merged or nested headers lead to misaligned values. ([Venturebeat][1])
- Revamped or revised documents create ambiguity (which version counts?) — agents often stop at a “plausible” answer rather than the most authoritative one. ([Venturebeat][1])
- Around 3% of OfficeQA questions require interpreting charts or graphs — a visual reasoning weakness where agents consistently fail. ([Venturebeat][1])
In short, many AI models are better suited for well‑structured text and code than for the messy, multi-format data that companies actually work with.
Why This Matters — For Enterprises and AI Developers
For organizations building AI‑powered document-processing tools — whether for finance, compliance, operations, or reporting — these results are a sobering reminder:
- Don’t trust marketing claims. Even state-of-the-art agents yield <50% accuracy on raw PDFs.
- Plan for human‑in‑the‑loop or custom parsing. Off-the-shelf LLMs likely won’t cut it without additional infrastructure for robust parsing and quality checks.
- Benchmark on realistic data, not toy problems. For real-world readiness, systems should be tested on document complexity similar to what they’ll face in production — not idealized code challenges or math puzzles.
For AI researchers and vendors, OfficeQA signals a shift: success is no longer about passing abstract tests — it’s about building systems that can wrangle messy enterprise data reliably and deterministically.
Glossary
- Benchmark: A standardised test or dataset used to evaluate and compare performance of AI models.
- Grounded reasoning: The ability of an AI to answer questions based on actual data or documents — not by recalling memorised facts or using web search.
- Parsing: The process of reading and converting raw document formats (PDFs, scans, tables) into structured data that AI models can understand and reason over.
- LLM (Large Language Model): A type of AI model (like GPT-5.1 or Claude) trained on massive text corpora, capable of generating and reasoning about human-like text.
In a world where enterprises are increasingly looking to AI to automate complex document workflows, the findings of OfficeQA are a crucial wake‑up call. The frontier isn’t abstract reasoning — it’s the gritty, unglamorous challenge of real‑world document parsing and data accuracy.
| [1]: https://venturebeat.com/data-infrastructure/databricks-officeqa-uncovers-disconnect-ai-agents-ace-abstract-tests-but “Databricks’ OfficeQA uncovers disconnect: AI agents ace abstract tests but stall at 45% on enterprise docs | VentureBeat” |
| [2]: https://www.databricks.com/blog/introducing-officeqa-benchmark-end-to-end-grounded-reasoning “Introducing OfficeQA: A Benchmark for End-to-End Grounded Reasoning | Databricks Blog” |
