Intelligent Multi-Agent System for Research Automation

Abstract

This project proposes a domain-aware multi-agent research system that intelligently routes user queries to specialized AI agents across multiple domains such as biomedical, legal, market research, academic research, and technical programming. Traditional large language models (LLMs) often generate hallucinated or unverifiable responses due to lack of domain-specific grounding and validation [3]. To address this limitation, the system introduces an intelligent Query Router that classifies queries using hybrid techniques such as keyword analysis and embedding similarity, assigning confidence scores and selecting the most relevant domain agents [1]. Each domain agent interacts with domain-specific data sources and generates structured, citation-backed responses. A validation layer further verifies outputs to ensure reliability and reduce hallucinations. The system is implemented using Node.js, LLM APIs, and databases such as MongoDB or PostgreSQL. It will be evaluated using domain-specific benchmarks, demonstrating improved accuracy, efficiency, and reliability compared to traditional single-agent systems [2].

Introduction

Large Language Models (LLMs), such as GPT-4, have significantly improved automated question-answering by generating contextually relevant and human-like responses across various domains. However, they often suffer from hallucinations, producing factually incorrect or unverifiable information with high confidence. This limitation is especially critical in fields such as healthcare, law, and scientific research, where accuracy is essential. Retrieval-Augmented Generation (RAG) addresses this issue by grounding responses in external knowledge sources, but most existing RAG systems are designed for a single domain and struggle with cross-domain research tasks. To overcome these limitations, the proposed work introduces a domain-aware multi-agent research system that combines intelligent query routing, specialized domain agents, and a validation layer to produce accurate, structured, and citation-backed responses while reducing hallucinations.

The existing problem lies in the widespread use of monolithic AI models that lack domain-specific expertise, often resulting in inaccurate responses, inefficient query handling, and increased computational overhead. General-purpose systems fail to intelligently distinguish between different knowledge domains, leading to improper routing and reduced response quality. Therefore, the project aims to develop an intelligent and scalable research platform capable of identifying the domain of a query, routing it to the most relevant specialized agents, validating generated responses, and efficiently supporting both single-domain and cross-domain queries. The objectives include designing a domain-aware multi-agent architecture, implementing intelligent routing using embeddings and keyword analysis, developing specialized agents, incorporating a validation mechanism to minimize hallucinations, generating structured outputs with citations, and evaluating performance using benchmark datasets.

The literature review highlights recent advancements in multi-agent systems, intelligent routing strategies, Retrieval-Augmented Generation, and hallucination reduction techniques. Previous works such as MasRouter, AgentRouter, RIRS, Agentic RAG, and Chain-of-Agents have contributed to efficient routing and reasoning, while frameworks like Toolformer, ReAct, LangChain, MRKL, and GPT-based systems have improved tool usage and reasoning capabilities. However, existing solutions often lack one or more critical components, including domain-specific routing, validation mechanisms, structured output generation, or scalability across multiple domains. The proposed system addresses these research gaps by integrating intelligent routing, modular domain-specific agents, response validation, and structured knowledge synthesis into a unified framework.

The proposed methodology consists of four major stages. First, the Query Processing and Routing module preprocesses user queries using natural language processing techniques and converts them into embedding representations. A hybrid routing mechanism combines keyword matching and embedding similarity to determine the most relevant domain-specific agents while assigning confidence scores. This selective routing improves computational efficiency by avoiding unnecessary processing.

Second, the Domain-Specific Agent Processing stage utilizes specialized agents dedicated to different knowledge areas. The Biomedical Agent retrieves evidence from PubMed, the Legal Agent accesses legal case databases, the Market Research Agent gathers financial and news information, the Academic Agent retrieves research papers from sources such as arXiv and Semantic Scholar, and the Technical Agent accesses programming documentation and code repositories. Each agent follows a three-step workflow involving document retrieval, response generation using LLM APIs, and structured formatting of the results.

Third, a Fact-Checking and Validation Layer is introduced to improve response reliability. This component verifies generated answers by checking citation authenticity, source credibility, and information recency. If inconsistencies are detected, the response is either flagged or regenerated, thereby reducing hallucinations and improving trustworthiness.

Finally, the Response Aggregation and Structuring module combines outputs from multiple agents into a unified response containing a concise summary, key findings, domain-specific insights, references, and confidence scores. This enables effective handling of complex cross-domain queries while maintaining clarity and traceability.

The system will be implemented using Node.js and Express.js for backend development, with React.js for the frontend. LLM APIs such as OpenAI, Gemini, or Anthropic will be integrated for response generation, while MongoDB or PostgreSQL will store logs and intermediate data. Vector databases such as FAISS or Pinecone will support semantic search, and external APIs including PubMed, arXiv, GitHub, and News APIs will provide domain-specific knowledge. The platform requires a minimum of 16 GB RAM, a multi-core processor, 100 GB of storage, and a high-speed internet connection.

The expected outcome is a scalable and modular AI-powered research platform capable of efficiently answering complex queries across multiple domains with improved accuracy, reduced hallucinations, and lower computational overhead. Intelligent routing is expected to decrease response latency, while specialized agents will generate evidence-based, citation-supported answers. The validation layer will enhance the credibility of responses by verifying information against trusted sources. System performance will be evaluated using biomedical, legal, and programming benchmark datasets, with metrics such as accuracy, routing precision, citation correctness, and response time. Overall, the proposed architecture aims to provide a reliable, interpretable, and scalable alternative to traditional single-model AI systems for advanced research applications.

References

[1] Yue et al.; MasRouter; Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), 2025. [2] Wu et al.; RIRS Multi-Agent QA; arXiv preprint arXiv:2402.01680 (2025). [3] Sadat et al.; DELUCIONQA; Findings of the Association for Computational Linguistics (ACL), 2023. [4] Chen et al.; Multi-Agent Medical LLMs; NPJ Digital Medicine (Nature), 2025. [5] Hao et al.; LLM Agents in Bioinformatics; Briefings in Bioinformatics, 26(6), bbaf601 (2025). [6] AgentRouter; arXiv preprint arXiv:2510.05445 (2025). [7] RCR Router; arXiv preprint arXiv:2508.04903 (2025). [8] MultiAgentBench; arXiv preprint arXiv:2503.01935 (2025). [9] Agentic Retrieval-Augmented Generation (Agentic RAG); arXiv preprint arXiv:2501.09136 (2025). [10] Chain-of-Agents; arXiv preprint arXiv:2406.02818 (2024). [11] LLM Agent Survey; arXiv preprint arXiv:2402.01680 (2024). [12] Toolformer; arXiv preprint arXiv:2302.04761 (2023). [13] ReAct: Synergizing Reasoning and Acting in Language Models; arXiv preprint arXiv:2210.03629 (2022). [14] GPT-4 Technical Report; arXiv preprint arXiv:2303.08774 (2023). [15] LangChain Documentation; LangChain Official Documentation, 2023. [16] PaperQA; arXiv preprint arXiv:2312.07559 (2023). [17] Hallucination Survey; arXiv preprint arXiv:2304.14779 (2023). [18] MRKL Systems: A Modular Neuro-Symbolic Architecture; arXiv preprint arXiv:2205.00445 (2022). [19] Domain Question Answering Systems; IEEE Xplore Digital Library, 2021. [20] Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks; arXiv preprint arXiv:2005.11401 (2020). [21] GPT-3: Language Models are Few-Shot Learners; arXiv preprint arXiv:2005.141 (2020).

Copyright

Copyright © 2026 Siddhant Lohakar , Annlin Mathew , Atharva Kamde , Sakshi Bondre, Prof. Dhirajkumar Gupta. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.