Most interactions with large language models feel like a simple conversation. You ask a question, the model gives an answer. This is a linear, one-shot process. The model takes your prompt, processes it, and returns a single block of text. Once it's done, it waits for the next prompt. This is the foundation, but it's not where the real power lies.

Agentic workflows transform this static exchange into a dynamic, multi-step process. Instead of just answering questions, an AI agent can pursue a goal. It can reason about what it needs to do, take actions in a digital environment, observe the results, and then decide on its next move. This is an iterative loop, not a straight line.

This shift from passive text generation to active problem-solving is what defines an agentic system. The model isn't just a language tool anymore; it's the core reasoning engine of an autonomous agent.

The core of an agent's behavior is a simple but powerful cycle: Reason, Act, and Observe. This is often called the ReAct loop. It’s what allows an agent to break down a big problem into smaller pieces and tackle it step-by-step, learning as it goes.

Imagine you ask an agent to "find the current price of gold and tell me if it's a good time to buy." A simple chatbot might give you a generic answer. An agent using the ReAct loop would tackle this differently.

1. Reason: The LLM recognizes it needs two pieces of information: the current price and expert analysis. It forms a plan: first, find the price; second, search for financial news about gold.
2. Act: The agent executes its first step. It uses a tool, like a financial data API or a web search function, to look up the price of gold.
3. Observe: It receives the result: "Gold is $2,350 per ounce." It stores this information in its short-term memory.

Now the loop repeats.

1. Reason: The agent knows it has the price. The next step is to find market sentiment. It refines its plan.
2. Act: It performs a web search for "gold market analysis today."
3. Observe: It gets back headlines and summaries of financial articles. It processes this new information.

Finally, the agent uses all the information it gathered to synthesize a final answer for you. This ability to plan, act, and adapt is what makes agents so capable.

To make the ReAct loop possible, an agent needs a specific architecture. Think of it as a blueprint with four essential pillars.

While that list is comprehensive, we can group these into four core functional areas:

• LLM (Reasoning Engine): This is the agent's brain. The Large Language Model is responsible for understanding the user's goal, making decisions, and forming plans. It's the component that does the "reasoning" in the ReAct loop.

• Tools (Action Interface): Agents need a way to interact with the world beyond generating text. Tools are functions or APIs that the agent can call to perform actions. This could be anything from a web search, a calculator, a database query, or even sending an email.

• Memory: An agent needs to remember what it has done and what it has learned. Memory can be short-term, like the conversation history within a single session, or long-term, allowing it to retain information across many interactions.

• Planning & Reflection: This is the logic that guides the agent. It involves breaking down a high-level goal into a sequence of actionable steps (Planning) and evaluating the outcomes of actions to adjust the plan (Reflection). This orchestrates the entire ReAct loop.

Statefulness is crucial. Without it, an agent would start fresh with every turn of the loop, unable to learn from its previous steps. Memory provides this state, making the agent's behavior coherent and goal-directed.

The key difference between a simple automation script and an AI agent is autonomy. A script follows a predefined, rigid set of instructions. If something unexpected happens, it fails. An agent, however, is designed to handle the unexpected.

Because the LLM is making decisions at each step, an agent can adapt its plan based on new information. If an API call fails, it can reason about the error and try a different approach. If its first web search yields no results, it can try a different query. This is the essence of autonomy: the ability to make decisions and adjust a course of action to achieve a goal without constant human direction.

This ability to operate independently is what allows agentic systems to move beyond simple question-answering and into the realm of proactive problem-solving in complex, real-world environments. They don't just process information; they interact with systems to get things done.

Let's review the key concepts we've covered.

Now, check your understanding of these foundational ideas.

With this foundation, we can begin to explore how these agents are constructed and the different patterns they use to solve problems.