Agentic AI refers to artificial intelligence systems capable of autonomous reasoning, planning, and execution to achieve high-level goals with limited human supervision.

Unlike "passive" AI models (like a standard chatbot) that simply respond to a user's input and then stop, Agentic AI operates in a continuous loop. It can break a complex objective into a sequence of smaller tasks, use external tools (like web browsers, code interpreters, or APIs) to perform actions, observe the results of those actions, and adapt its plan if things go wrong.

To function effectively, Agentic AI typically relies on an architecture often described as a "cognitive architecture," comprising:

• Perception: The ability to read inputs, browse the web, or access files to understand the current state of the task.
• Planning (Reasoning): Using logic to decompose a broad goal (e.g., "Plan a marketing campaign") into executable steps (e.g., "Research competitors," "Draft copy," "Generate images").
• Tool Use (Action): The ability to interface with other software. For example, an agent might write and execute Python code to analyze data or use an API to send an email.
• Memory & Reflection: Remembering past actions and critiquing their own outputs to correct errors before presenting a final result.

Strategic Impact: Agentic AI represents a shift from "Chatbots" to "Action-bots." This transition moves AI from being a tool for information retrieval to a proactive workforce capability. However, it introduces significant control risks. Because agents act autonomously, they can potentially enter infinite loops, spend excessive resources (e.g., cloud API costs), or execute harmful actions (like deleting a database) if not constrained by strict permission boundaries and "human-in-the-loop" safeguards.