Agentic Search

Traditional search requires users to type keywords, scan a results page, and manually extract what they need. Agentic search inverts that workflow: the user states a goal, and the agent decomposes it into sub-queries, explores multiple sources, compares findings, and returns a consolidated answer or a completed action. Gartner projects that by the end of 2026, 40% of enterprise applications will include task-specific agents, up from under 5% a year earlier. This shift fundamentally changes how content gets discovered and consumed.

Perplexity Computer: Orchestrates 19 AI models simultaneously, routing each sub-task to the optimal model. Integrates with 400+ apps and can persist tasks for hours, days, or months.

OpenAI Operator: A browser-controlling agent that navigates websites, fills forms, and completes bookings and purchases on the user's behalf.

Microsoft Azure AI Search: Offers agentic retrieval where an LLM intelligently decomposes complex queries for enterprise search scenarios.

In an agentic search world, users visit fewer websites directly because agents gather and process information as intermediaries. For content to be selected by agents, structured data, clear factual statements, and credible source attribution become more important than ever. Preparing for the agentic web means building content structures that machines can parse and cite reliably.