Agentic Search
Agentic search is when an AI system autonomously plans and runs multiple search and reasoning steps to answer a complex question, rather than retrieving once and replying.
Agentic search is an AI that researches in steps, not one shot. Instead of a single retrieve-then-answer pass, an agentic system plans a strategy, runs several searches, reads results, decides what's still missing, and searches again — iterating toward a thorough answer much as a human researcher would.
It raises the stakes of broad, well-structured coverage. Like query fan-out, agentic search touches many sub-questions, so a site that comprehensively and clearly answers a topic has more chances to be encountered and cited across the agent's path. Because these systems are evolving fast, the adaptability pillar applies: watch how multi-step engines surface you and adjust. The foundation is unchanged — be retrievable and be the best answer at each step.
Example. Asked to "find and compare the three best CRMs for a 5-person agency," an agentic system might search each tool, read reviews, and check pricing pages before answering — citing a different source at each stage of its research.
Relevant pillar
Related terms
- Query Fan-OutQuery fan-out is when an AI engine takes one user question and silently expands it into several related searches, then synthesizes one answer from everything it retrieves across them.
- Answer EngineAn answer engine is a search system that responds to a question with a direct, synthesized answer instead of a list of links, usually citing the sources it drew from.
- RAG (Retrieval-Augmented Generation)RAG is the technique behind most AI answer engines, where the model first retrieves relevant documents from the live web or an index and then generates an answer grounded in what it found.