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Deep Research Agents: A Systematic Examination And Roadmap
Created by
Haebom
Author
Yuxuan Huang, Yihang Chen, Haozheng Zhang, Kang Li, Huichi Zhou, Meng Fang, Linyi Yang, Xiaoguang Li, Lifeng Shang, Songcen Xu, Jianye Hao, Kun Shao, Jun Wang
Outline
This paper presents a detailed analysis of the fundamental technologies and architectural components of a Deep Research (DR) agent. A DR agent is an autonomous AI system designed to perform complex, multi-step information research tasks by combining dynamic reasoning, adaptive long-term planning, multi-stage information retrieval, iterative tool utilization, and structured analysis report generation. We compare API-based and browser-based search methods, review a modular tooling framework, and explore code execution, multi-modal input processing, and integrate the Model Context Protocol (MCP) to support scalability and ecosystem development. We propose a taxonomy that distinguishes between static and dynamic workflows and categorize agent architectures based on planning strategies and agent configurations, including single-agent and multi-agent configurations. We also highlight key limitations of current benchmarks, including limited access to external knowledge, sequential execution inefficiencies, and mismatches between evaluation metrics and the actual goals of DR agents. We also suggest open challenges and promising directions for future research. We also provide a continuously updated DR agent research repository ({ https://github.com/ai-agents-2030/awesome-deep-research-agent}) .
Takeaways, Limitations
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Takeaways:
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Provides a systematic analysis of the underlying technologies and architecture of DR agents.
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Provides insight into API-based and browser-based information acquisition strategies, modular tooling frameworks, and diverse agent architectures.
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We present Limitations, a classification scheme and benchmark for DR agent research.
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It presents open challenges and promising directions for future research.
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Provides a community-based repository for DR agent research.
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Limitations:
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Although it points out problems with current benchmarks, such as limited access to external knowledge, inefficiency in sequential execution, and mismatch between evaluation metrics and actual goals, it does not propose specific solutions to address these issues.
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Although various DR agent architectures have been classified, comparative analysis of the pros and cons of each architecture is lacking.
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Further validation of the objectivity and generalizability of the classification scheme and benchmarks presented in the paper is needed.
