A standard Large Language Model (LLM) is like a brilliant but forgetful expert. You ask a question, it gives a single, well-articulated answer based on its training, and the interaction ends. This is a one-shot process. To move from simple Q&A to autonomous problem-solving, we need a way for the model to think, act, and learn within a single task. We need to turn the static prompt-response into a dynamic loop.

This is where agentic workflows come in. Instead of just generating a final answer, an AI agent breaks a goal down into a series of internal thoughts and external actions. It can use tools, access new information, and even correct its own mistakes along the way. The core mechanism driving this is a reasoning loop, a continuous cycle of thought, action, and observation.

One of the most foundational patterns for this process is called ReAct, which stands for "Reason + Act." It's an elegant way to structure an agent's workflow by having the LLM explicitly verbalize its thinking process before it does anything. At each step, the model generates three things:

1. Reason: A private thought about the current situation, the overall goal, and what to do next.
2. Act: The specific action to take, like calling a web search API with a certain query or running a piece of code.
3. Observe: After the action is executed, the result (e.g., the search results, an error message) is fed back into the model's context as an observation.

This cycle repeats. The observation from step one informs the reason in step two, which leads to a new action, and so on. The agent continues this process until it reasons that it has enough information to provide a final answer to the user.

Think of a detective solving a mystery. They don't just guess the culprit. They start with a goal (Reason: "I need to find the lead suspect."), take an action (Act: "Search the victim's phone records."), and then analyze the results (Observe: "The last call was to an unknown number."). This new information fuels the next cycle of reasoning.

For more complex tasks, a simple, reactive loop isn't always enough. An agent might need a more comprehensive strategy. This leads to Plan-and-Execute architectures. In this model, the agent's process is split into two distinct phases:

1. Planning: First, the agent analyzes the user's high-level goal and performs an initial round of autonomous goal decomposition. It breaks the main objective into a sequence of smaller, concrete steps. For example, the goal "Plan a weekend trip to San Diego" might be broken down into: "Find flights," "Book a hotel," "List three popular attractions," and "Find a highly-rated seafood restaurant."

2. Execution: Once the plan is set, the agent executes each step sequentially. Crucially, each individual step in the plan can be carried out using its own ReAct-style reasoning loop. To "Find flights," the agent might reason about the best travel site, act by searching it, and observe the results before moving to the hotel step.

This approach combines the foresight of planning with the reactive flexibility of a reasoning loop. The plan provides a high-level roadmap, while the loops handle the messy details of execution and allow for iterative self-correction. If a tool fails or a search returns bad data during an execution step, the loop allows the agent to try again with a different approach without derailing the entire plan. This ability to reflect and retry is what makes agentic systems so resilient.

With these reasoning patterns, we shift the LLM's role from a simple text generator to the core cognitive engine of a system that can plan, act, and adapt. It's the fundamental building block for creating AI that can tackle complex, multi-step tasks in the real world.