Deep Research Agents: A Systematic Examination And Roadmap

Key points

• Beyond existing LLMs and RAG, there is no clear definition or taxonomy for a new class of AI systems that integrate dynamic reasoning, adaptive planning, and iterative tool use.
• There is a need to unify scattered research efforts into a single framework in order to understand common architectural patterns and foundational technologies.
• Existing evaluation benchmarks do not adequately assess the full end-to-end capabilities of advanced AI research agents.
• This paper formally defines deep research, or DR, agents and proposes a unified taxonomy that distinguishes static from dynamic workflows and single-agent from multi-agent systems.
• It systematically analyzes foundational technologies, including information acquisition strategies such as API-based versus browser-based approaches, diverse tool-use capabilities such as code interpreters and multimodal processing, and memory mechanisms for optimizing long context.
• It evaluates current tuning paradigms such as SFT, RL, and nonparametric continual learning, reviews key industry implementations, and critically assesses existing benchmarks for DR agents.

Paper links

External research summaries. These are not HDATF publications or measured product results.