Human clicks are a proxy for relevance shaped by laziness and UI convenience. For AI agents, which need authoritative and precise information, this signal is noisy and misleading. Agentic search should rely on feedback from the agent's task success, not human browsing habits.

Google is integrating AI agents directly into search, allowing users to create ongoing tasks like monitoring apartment listings. This transforms search from a tool for one-time information retrieval into a persistent service that works 24/7, a fundamental shift in its core function and user interaction model.

Instead of indexing all data into a vector database, AI agents can connect to standard APIs and run numerous queries in parallel, refining results iteratively. This trades speed and compute cost for flexibility and avoids heavy upfront infrastructure setup, changing the search paradigm.

Unlike humans who type 2-3 words, LLMs generate long, sentence-like queries (e.g., eight words or more) to gather comprehensive context. This shift in user behavior from human to AI requires search engines to be optimized for these detailed, descriptive inputs.

Google's VP of Search notes that AI enables users to state their complex needs in natural language, rather than translating them into keywords. Users now "tell you the real problem," providing Google with richer intent data to deliver more helpful and specific results.

AI agents, unlike humans, need complete and exhaustive information (thousands of results) and use complex, controllable queries. A search engine built for human keyword simplicity and limited results will fail to serve them effectively.

Instead of being replaced by AI chatbots or agents, Pichai believes Search will evolve to manage them. Users will run multiple, long-running tasks, and Search will become the interface to orchestrate these agentic flows, expanding its capabilities rather than becoming obsolete.

While Google SEO relies heavily on placing keywords in specific technical elements like title tags, AI search engines care less about keywords. They prioritize content that directly and comprehensively answers a user's question. The strategy shifts from keyword density to providing the best possible solution.

Unlike chatbots that rely solely on their training data, Google's AI acts as a live researcher. For a single user query, the model executes a 'query fanout'—running multiple, targeted background searches to gather, synthesize, and cite fresh information from across the web in real-time.