We introduce OfficeQA, a benchmark that evaluates end-to-end grounded reasoning over U.S. Treasury Bulletins spanning January 1939 through September 2025. The benchmark consists of 697 PDFs that are around 100-200 pages long with the corpus spanning over 89,000 pages and consisting of scanned PDFs. While these bulletin documents are available publicly, the benchmark is intentionally constructed to be challenging because most required facts live inside the corpus and require accurate parsing and retrieval of such documents to perform accurate reasoning, rather than present completely in the parametric knowledge of state-of-the-art LLMs or even general web search. Each task requires an agent to locate the relevant source material, extract precise values from real world tables and figures through document parsing, and then execute multi step computations to produce a single verifiable output. The difficulty distribution of this benchmark spans elementary extraction and arithmetic through long chain quantitative reasoning across multiple documents and statistical analysis that leverage inherent coding abilities (e.g. financial forecasting, econometrics, etc.), comprising of a 46% easy / 54% hard split as validated by real human annotators crafting the 246 total questions. The evaluation of this task is designed to be objective and reproducible by ensuring all answers are verifiable and resolved to a single value, values or a short string. The green agent serves as the judge, running a deterministic evaluation harness and scoring predictions at 0.0 tolerance through a fuzzy match for formatting differences (unit normalization, numeric parsing for commas, percents, etc., and extraction of the final answer separated from the full agent reasoning and response trace). This yields a clear pass rate metric that reflects whether a system can complete the full pipeline from document grounded extraction to correct computation. (Notably, the baseline purple agents (gpt5.2 and claude-opus-4-5 with no tools) tested are expected to perform poorly since they are not directly provided access to the documents in a file system, demonstrating the challenges of this task without having the parametric information known to LLMs while also requiring agentic capabilities like parsing, retrieval, and reasoning to achieve high accuracy. As a demonstration, we test a configuration of the baseline agents having access to the web search tool, which demonstrates some level of non-determinism due to the nature of web search retrieval, but still hovers around consistent reproducibility ranges. In future true purple agents to demonstrate hill-climbing on this benchmark, we will test agent systems like Claude Agent SDK, OpenAI Agent SDK, Google ADK, and other tool-specific solutions like state-of-the-art parsing systems, file search, retrieval and vector store solutions and other constructions of agentic systems. ) Blog Post: https://www.databricks.com/blog/introducing-officeqa-benchmark-end-to-end-grounded-reasoning