mlsanityData to 14 Oct 2026

Retrieval-Augmented Multi-Agent System for Rapid Statement of Work Generation

arXiv 2508.07569 ↗First posted 11 Aug 2025cs.MA cs.AIcited by 1

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Abstract (arXiv)

Drafting a Statement of Work (SOW) is a vital part of business and legal projects. It outlines key details like deliverables, timelines, responsibilities, and legal terms. However, creating these documents is often a slow and complex process. It usually involves multiple people, takes several days, and leaves room for errors or outdated content. This paper introduces a new AI-driven automation system that makes the entire SOW drafting process faster, easier, and more accurate. Instead of relying completely on humans, the system uses three intelligent components or 'agents' that each handle a part of the job. One agent writes the first draft, another checks if everything is legally correct, and the third agent formats the document and ensures everything is in order. Unlike basic online tools that just fill in templates, this system understands the meaning behind the content and customizes the SOW to match the needs of the project. It also checks legal compliance and formatting so that users can trust the result. The system was tested using real business examples. It was able to create a full SOW in under three minutes, compared to several hours or days using manual methods. It also performed well in accuracy and quality, showing that it can reduce legal risks and save a lot of time. This solution shows how artificial intelligence can be used to support legal and business professionals by taking care of routine work and helping them focus on more important decisions. It's a step toward making legal processes smarter, faster, and more reliable.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 14 references that are in the map. First posted on arXiv 11 Aug 2025.

Builds on

8 of 14 references.

References · 8

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  3. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  4. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  5. 2023A Comprehensive Overview of Large Language Modelscited by 648
  6. 2024Agent AI with LangGraph: A Modular Framework for Enhancing Machine Translation Using Large Language Modelscited by 28
  7. 2025CoSTAast: Cost-Sensitive Toolpath Agent for Multi-turn Image Editingcited by 6
  8. 2024Dynamic Multi-Agent Orchestration and Retrieval for Multi-Source Question-Answer Systems using Large Language Modelscited by 3

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Citing papers per year: 2026: 1.

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  1. 2025AI Agents-as-Judge: Automated Assessment of Accuracy, Consistency, Completeness and Clarity for Enterprise Documents
  2. 2026Agentic AI and Retrieval-Augmented Models in Straight-Through Underwriting
  3. 2026Schematize: An Agentic System for Generating and Refining Information-Extraction Schemas for Legal Research
  4. 2026GUIDE: Governed Unified Intelligence for Document-to-Artifact Generation in Enterprise Settings
  5. 2026NyayaAI: An AI-Powered Legal Assistant Using Multi-Agent Architecture and Retrieval-Augmented Generation
  6. 2025AI for Statutory Simplification: A Comprehensive State Legal Corpus and Labor Benchmark
  7. 2026HR-Agents: Using Multiple LLM-based Agents to Improve Q&A about Brazilian Labor Legislation
  8. 2024It Cannot Be Right If It Was Written by AI: On Lawyers' Preferences of Documents Perceived as Authored by an LLM vs a Human