This document proposes Agentic Information Retrieval (Agentic IR), a new paradigm for information retrieval that utilizes Large Language Models (LLMs) as agents to actively interact with users and their environments to fulfill information needs. This new paradigm contrasts with traditional IR systems, which passively filter information and present it to users. Agentic IR expands the task scope, employs a unified architecture, and incorporates new methods such as prompt engineering, retrieval-augmented generation, and reinforcement learning to enhance user interaction and task completion. While still facing challenges in data acquisition, model training, and safety, the authors argue that Agentic IR has the potential to revolutionize information retrieval and become a central information entry point in future digital ecosystems. Examples of this new paradigm include life assistants, business assistants, and coding assistants that proactively support users in their respective domains.