Large language models are powerful consultants. You ask a question, and they provide a well-reasoned answer based on their training data. But what if an AI could do more than just answer? What if it could act on your request, break it down into steps, use tools to find new information, and work autonomously until the task is complete? This is the shift from a passive LLM to an active, or agentic, AI.

Agentic AI doesn't just process information; it interacts with its environment to achieve a goal. It moves beyond a simple, one-time response and enters a continuous cycle of operation, much like a human worker tackling a complex project.

At the heart of an agentic system is a loop. The AI isn't just waiting for the next prompt; it's actively working through a cycle of perception, thought, and action. A useful mental model for this is the OODA loop, a concept originally from military strategy that applies perfectly to AI agents.

OODA stands for Observe, Orient, Decide, and Act. It’s a continuous cycle that allows the agent to process information and respond to its environment dynamically.

Let’s break down the loop:

1. Observe: The agent perceives its environment. This isn't about seeing or hearing, but about taking in data. It could be a user's initial goal, the output from a code compiler, data from a web API, or the current state of a file system.

2. Orient: This is the reasoning step. The agent analyzes the new information in the context of its current goal. It accesses its memory—both short-term (like the history of the current conversation) and long-term (knowledge it has stored from past tasks)—to make sense of the situation.

3. Decide: Based on its orientation, the agent plans its next move. It doesn't just guess. It performs task decomposition, breaking a large, complex goal into smaller, manageable sub-tasks. For example, the goal