A new category of independent AI systems, referred to as Deep Research (DR) agents, has been developed to tackle complex, multi-turn information research challenges. These agents leverage dynamic reasoning, long-term adaptive planning, comprehensive information retrieval from various sources, and produce detailed analytical reports. A study examines the underlying technologies and structural components that support these agents.
This research critiques different information gathering techniques, contrasting API-based retrieval with web-based exploration. It investigates modular frameworks for tool applications, including code execution, multimodal input processing, and integration of Model Context Protocols for better adaptability. To classify existing methods, a taxonomy separates static and dynamic workflows and categorizes different agent architectures by their planning strategies.
Moreover, the study assesses current benchmarks and points out critical limitations like restricted access to outside knowledge, inefficiencies in sequential processing, and misalignment of evaluation metrics with DR agents’ real-world objectives. The findings lead to discussions on challenges and highlight promising areas for future DR exploration.
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