From Finding to Doing: What Is 'Agentic' Search?
The classic search engine is a library catalog for the internet; it tells you where to find information, but you have to go get it yourself. Agentic search, on the other hand, is like sending a capable assistant to do the work for you. [14, 7] Instead
of just typing 'budget hotels in Goa', you can now ask an AI agent, 'Plan a three-day budget trip to Goa from Delhi for two people, find hotels under ₹5,000 with good reviews, and show me flight options.' The agent doesn't just return links; it understands your goal, breaks it into steps, gathers information, compares options, and presents a synthesized answer or even helps complete the booking. [1, 14, 4] This marks a fundamental shift from information retrieval to autonomous task execution. [17] The user is no longer a searcher, but a director, delegating complex jobs to an AI that acts on their behalf.
The Engine Behind the Curtain: AI Models That Act
This new era is powered by advancements in Large Language Models (LLMs) and the development of 'agentic' AI systems. [17] An AI agent is more than just a chatbot. It's a system designed to perceive a goal, create a plan, take actions using digital tools (like browsing a website or accessing an API), and learn from the results to complete its task. [17, 7] Frameworks like ReAct (Reason and Act) enable these systems to reason through a problem and execute a sequence of actions. [19] For example, when you ask it to plan a trip, the agent might first search for flights, then search for hotels based on those dates, and then look up reviews for the hotels it found, all without constant human supervision. [1, 19] This ability to perform multi-step, autonomous tasks is what separates a simple conversational AI from a true AI agent.
The New Players on the Field
This shift isn't just theoretical; new tools are already changing user behavior. Perplexity AI has emerged as a key player, functioning as a 'research engine' that delivers synthesized answers with inline citations from live web sources. [9, 2] It excels at deep, research-oriented queries where transparency and sources are critical. [2] Meanwhile, established giants are not standing still. Google has integrated AI Overviews into its main search results, providing AI-generated summaries at the top of the page for many queries. [3] At its recent I/O conference, Google made it clear that it sees the future of search as 'agentic', with its systems helping users manage and complete tasks. [5, 8] Microsoft's Copilot and startups like Gumloop and CrewAI are also building platforms that allow for the creation and deployment of these AI agents, signaling a market-wide move toward this new paradigm. [22, 26]
A Web without Clicks?
The rise of agentic search poses a disruptive question: What happens to the web if users no longer need to click on websites? [15] When an AI agent provides a direct answer, it can reduce or eliminate the need for a user to visit the source page, a phenomenon known as 'zero-click' searches. [11] This has significant implications for online business models built on advertising and traffic. [21] Early data suggests that AI summaries can lead to a drop in organic search traffic for many sites. [21, 13] Consequently, the focus for businesses is shifting from traditional Search Engine Optimization (SEO) to a new discipline sometimes called Answer Engine Optimization (AEO). [11] The goal is no longer just to rank highly, but to have your data structured and trustworthy enough for AI agents to use and cite it in their answers. [7, 15] In this new world, the first audience for your website might not be a human, but a machine.
