Agentic Search
An AI-driven search method where an autonomous agent plans, executes, and refines a multi-step search process to answer complex queries, rather than performing a single, static lookup.
What is Agentic Search?
Agentic Search is an AI-powered search methodology where an autonomous agent (typically driven by a Large Language Model) plans, executes, and refines a multi-step search process to answer complex queries, rather than performing a single, static lookup.
Why Agentic Search Matters
In practical applications, Agentic Search helps solve complex research and reasoning tasks by breaking down a broad query into sequential sub-queries. Its primary mechanism lies in self-correction and iterative refinement: if the initial retrieval returns incomplete or conflicting data, the agent dynamically reformulates its queries and searches again. This closed-loop process significantly reduces AI hallucinations and ensures a more comprehensive, verified output, making it highly effective for market research, academic analysis, and factual due diligence.
An Analogy of Agentic Search
While traditional search is like asking an assistant to find the current stock price of Company A, Agentic Search is like asking them to compile a comprehensive financial comparison of Companies A, B, and C over the past year. The AI agent will independently search for each company's data, cross-reference their financial reports, resolve any discrepancies, and synthesize a structured comparative report.