A standard large language model (LLM) interaction is a straight line. You give it a prompt, it generates a response. End of story. An AI agent is different. It doesn't just respond; it operates in a loop, turning a single complex objective into a series of smaller, manageable steps. This isn't a simple input-output chain; it's a dynamic cognitive architecture.

At the heart of this architecture is a reasoning engine, with the LLM acting as a central controller. Instead of just generating text, the LLM evaluates its own outputs, plans its next move, and decides which tools to use. This loop of thinking, acting, and observing is what separates a passive chatbot from an autonomous agent.

One of the most powerful frameworks for building agents is called ReAct, which stands for "Reasoning and Acting." It formalizes the loop we just described. The agent cycles through a sequence of thoughts, actions, and observations.

Imagine you ask an agent to plan a weekend trip to San Francisco. It won't just spit out an itinerary. Instead, it might follow a ReAct process:

1. Thought: The user wants a weekend trip plan. I need to find flights, book a hotel, and suggest activities.
2. Action: Use a flight search tool to check prices for the specified dates.
3. Observation: The tool returns several flight options. Flights on Friday are expensive.
4. Thought: The flights are pricey. I should check if leaving on Saturday morning is cheaper to save the user money. I'll also start looking for well-rated hotels near Fisherman's Wharf.
5. Action: Use the flight tool again with Saturday dates. Use a hotel search tool for the Fisherman's Wharf area.

This cycle continues until the goal is achieved. The agent isn't just executing a pre-written script; it's dynamically creating and adjusting its plan based on new information.

This reasoning loop would be useless if the agent couldn't remember its own thoughts and observations. Memory is the glue that holds the cognitive architecture together. For agents, we typically distinguish between two types: short-term and long-term.

Short-term memory, or working memory, is like a scratchpad. It holds the context for the current task: the user's initial request, the history of the conversation, and the results of recent actions. This information is usually passed directly into the LLM's context window with each turn of the loop.

Long-term memory gives the agent a persistent knowledge base that extends beyond a single session. This is often achieved using techniques like Retrieval-Augmented Generation. The agent can store key information, past conversations, or user preferences and retrieve them later to inform its reasoning. This allows the agent to learn and improve over time.

The final piece of the architecture is self-correction. An agent doesn't just act; it reflects on its actions. After an observation, the LLM evaluates the outcome. Was the action successful? Did it produce an error? Is it getting closer to the overall goal? This reflective step is a form of Chain-of-Thought reasoning, where the model analyzes its own performance to decide the next best action.

By combining the ReAct framework with short and long-term memory, the LLM serves as the central controller in a powerful cognitive loop. It reasons about goals, acts using tools, observes the results, and reflects on its progress before starting the cycle again. This continuous loop is what transforms a static model into an autonomous agent capable of solving complex, multi-step problems.

Let's test your knowledge of these core agent components.

Now that you understand how an agent thinks, you're ready to see how these individual agents can be combined into even more powerful systems.