AI Agents for Document Automation and Negotiation Between Buyers and Sellers

Muhammad Abdurrahman Ismath

Submission Track: Track 1: Machine Learning Research by Muslim Authors

Keywords: AI agents, document automation, negotiation systems, large language models, OCR, multi-agent systems, AI safety, trade efficiency, oil and gas trading, autonomous negotiation, natural language processing, intelligent document processing, compliance, digital contracts, commodity trade

TL;DR: A multi-agent system uses LLMs and OCR to automate trade document workflows and broker negotiations in commodity trading with built-in AI safety and audit features.

Abstract:

/begin{abstract}
The oil and gas trading industry faces protracted deal cycles due to labor-intensive document handling and prolonged negotiations. This research proposal introduces an AI agent-based workflow to streamline document automation and broker negotiations between buyers and sellers. The core is an \textit{AI Broker Document Handling Pipeline} that automates the extraction and structuring of trade documents and mediates communication between parties.

By leveraging optical character recognition (OCR) and large language models (LLMs) for intelligent document processing, and an AI broker agent to conduct negotiations with built-in guardrails, the system aims to dramatically compress deal timelines — from months to days — while maintaining trust and compliance. Specifically, the document processing component uses OCR to extract raw text and applies LLM-based parsing to structure key fields such as $quantity$, $price$, $delivery\_terms$, and $product\_grade$.

The AI Broker mediates negotiation using a multi-agent framework, where each party's preferences and constraints are modeled and exchanged iteratively under safety protocols. Let $T$ be the average deal completion time in traditional workflows and $T'$ be the projected time under the AI system. We aim to achieve $$T' \ll T$$ indicating a substantial efficiency gain.

We outline the problem, objectives, methodological framework, and evaluation plan for this approach. The expected impact is a significant reduction in time-to-deal closure and improved efficiency in oil and gas transactions, while preserving transparency, safety, and privacy.
\end{abstract}

Submission Number: 17