What is agentic search (and why cached results aren't enough)

Agentic search is a retrieval method that enables AI agents to query the live web in real-time, allowing them to access the most current data for reasoning and decision-making. Unlike traditional retrieval methods that rely on cached or pre-indexed data, agentic search dynamically formulates queries based on context and evaluates the content retrieved to refine its search, ensuring that AI agents can adapt to changes such as competitor pricing updates or new compliance requirements. This method is particularly useful for accessing volatile or unpredictable data sources, such as open-web content or real-time signals, and is implemented in tools like Firecrawl, which provides full-page content extraction in a single call. While agentic search is suited for dynamic content, Retrieval-Augmented Generation (RAG) is better aligned with stable, internal documentation, using a pre-built index for retrieval. Both methods can complement each other in production environments to provide comprehensive data coverage, with agentic search offering the advantage of open-ended discovery and grounded reasoning based on the latest available information.