Agentic search represents an advanced paradigm in information retrieval where an intelligent 'agent' autonomously plans, executes, and refines search queries and information synthesis steps. Unlike traditional retrieval-augmented generation (RAG) systems that often rely on static, one-shot retrieval, agentic search systems leverage iterative reasoning, dynamic query generation, and often navigate hierarchical data structures to explore complex information spaces. This approach is particularly crucial for tasks involving extremely long context windows, such as long video understanding, where naive chunking strategies lead to information fragmentation and a loss of global coherence. By enabling dynamic retrieval and reasoning across multiple levels of granularity, agentic search solves the problem of synthesizing comprehensive and coherent insights from vast, unstructured or semi-structured data. It is increasingly adopted in advanced AI systems for complex data analysis, knowledge discovery, and sophisticated question answering, particularly in domains like multimedia understanding and scientific research.
Agentic search is an advanced AI technique where a system intelligently and iteratively searches for information, often across organized data. It helps AI models understand very long and complex content, like videos, by dynamically finding and connecting relevant pieces of information to form a complete picture.
Agent-based search, Autonomous search, Iterative search agents
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