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ServiceNow AI Research Releases DRBench, a Realistic Enterprise Deep-Research Benchmark

ByRicardo

ServiceNow Research has launched DRBench, a benchmark and runnable surroundings to judge “deep analysis” brokers on open-ended enterprise duties that require synthesizing information from each public net and personal organizational knowledge into correctly cited experiences. Unlike web-only testbeds, DRBench phases heterogeneous, enterprise-style workflows—information, emails, chat logs, and cloud storage—so brokers should retrieve, filter, and attribute insights throughout a number of purposes earlier than writing a coherent analysis report.

https://arxiv.org/abs/2510.00172

What DRBench comprises?

The preliminary launch supplies 15 deep analysis duties throughout 10 enterprise domains (e.g., Sales, Cybersecurity, Compliance). Each process specifies a deep analysis query, a process context (firm and persona), and a set of groundtruth insights spanning three lessons: public insights (from dated, time-stable URLs), inside related insights, and inside distractor insights. The benchmark explicitly embeds these insights inside lifelike enterprise information and purposes, forcing brokers to floor the related ones whereas avoiding distractors. The dataset development pipeline combines LLM era with human verification and totals 114 groundtruth insights throughout duties.

https://arxiv.org/abs/2510.00172

Enterprise surroundings

A core contribution is the containerized enterprise surroundings that integrates generally used companies behind authentication and app-specific APIs. DRBench’s Docker picture orchestrates: Nextcloud (shared paperwork, WebDAV), Mattermost (group chat, REST API), Roundcube with SMTP/IMAP (enterprise e-mail), FileBrowser (native filesystem), and a VNC/NoVNC desktop for GUI interplay. Tasks are initialized by distributing knowledge throughout these companies (paperwork to Nextcloud and FileBrowser, chats to Mattermost channels, threaded emails to the mail system, and provisioned customers with constant credentials). Agents can function by way of net interfaces or programmatic APIs uncovered by every service. This setup is deliberately “needle-in-a-haystack”: related and distractor insights are injected into lifelike information (PDF/DOCX/PPTX/XLSX, chats, emails) and padded with believable however irrelevant content material.

Evaluation: what will get scored

DRBench evaluates 4 axes aligned to analyst workflows: Insight Recall, Distractor Avoidance, Factuality, and Report Quality. Insight Recall decomposes the agent’s report into atomic insights with citations, matches them towards groundtruth injected insights utilizing an LLM decide, and scores recall (not precision). Distractor Avoidance penalizes inclusion of injected distractor insights. Factuality and Report Quality assess the correctness and construction/readability of the ultimate report below a rubric specified within the report.

https://arxiv.org/abs/2510.00172

Baseline agent and analysis loop

The analysis group introduces a task-oriented baseline, DRBench Agent (DRBA), designed to function natively contained in the DRBench surroundings. DRBA is organized into 4 elements: analysis planning, motion planning, a analysis loop with Adaptive Action Planning (AAP), and report writing. Planning helps two modes: Complex Research Planning (CRP), which specifies investigation areas, anticipated sources, and success standards; and Simple Research Planning (SRP), which produces light-weight sub-queries. The analysis loop iteratively selects instruments, processes content material (together with storage in a vector retailer), identifies gaps, and continues till completion or a max-iteration price range; the report author synthesizes findings with quotation monitoring.

Why that is necessary for enterprise brokers?

Most “deep analysis” brokers look compelling on public-web query units, however manufacturing utilization hinges on reliably discovering the proper inside needles, ignoring believable inside distractors, and citing each private and non-private sources below enterprise constraints (login, permissions, UI friction). DRBench’s design immediately targets this hole by: (1) grounding duties in lifelike firm/persona contexts; (2) distributing proof throughout a number of enterprise apps plus the online; and (3) scoring whether or not the agent truly extracted the meant insights and wrote a coherent, factual report. This mixture makes it a sensible benchmark for system builders who want end-to-end analysis slightly than single-tool micro-scores.

https://arxiv.org/abs/2510.00172

Key Takeaways

  • DRBench evaluates deep analysis brokers on advanced, open-ended enterprise duties that require combining public net and personal firm knowledge.
  • The preliminary launch covers 15 duties throughout 10 domains, every grounded in lifelike person personas and organizational context.
  • Tasks span heterogeneous enterprise artifacts—productiveness software program, cloud file programs, emails, chat—plus the open net, going past web-only setups.
  • Reports are scored for perception recall, factual accuracy, and coherent, well-structured reporting utilizing rubric-based analysis.
  • Code and benchmark belongings are open-sourced on GitHub for reproducible analysis and extension.

Editorial feedback

From an enterprise analysis standpoint, DRBench is a helpful step towards standardized, end-to-end testing of “deep analysis” brokers: the duties are open-ended, grounded in lifelike personas, and require integrating proof from the public net and a personal firm information base, then producing a coherent, well-structured report—exactly the workflow most manufacturing groups care about. The launch additionally clarifies what’s being measured—recall of related insights, factual accuracy, and report high quality—whereas explicitly shifting past web-only setups that overfit to looking heuristics. The 15 duties throughout 10 domains are modest in scale however ample to reveal system bottlenecks (retrieval throughout heterogeneous artifacts, quotation self-discipline, and planning loops).

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