A standard large language model (LLM) is great at single-shot tasks. You provide a prompt, and it generates a response. This linear process works well for translation, summarization, or answering a direct question. But complex, open-ended goals require a different approach.

To solve multi-step problems, an AI needs to plan, access information, use tools, and correct itself when things go wrong. This is where agentic architecture comes in. Instead of a simple prompt-response chain, an agent operates in a continuous, iterative loop, creating a system that can reason its way toward a solution.

The core of agentic behavior is the ReAct pattern, which stands for Reason + Act. It's a simple but powerful idea that allows an LLM to mimic a structured thought process. Before taking any action, the agent first generates a rationale for why it's taking that action. This internal monologue is often called a reasoning trace.

This process creates a feedback loop. The agent thinks about what it needs to do, performs an action, observes the result, and then thinks again based on the new information. This cycle repeats until the goal is achieved.

Imagine you ask an agent: "What was the score of the last Super Bowl, and who was the MVP?" A non-agentic LLM might just guess. An agent using ReAct would break it down.

The reasoning trace makes the agent's process transparent and easier to debug. If the agent fails, you can examine its 'thoughts' to see where its logic went wrong. This is far more effective than trying to guess why a black-box model produced a strange output.

For goals more complex than a simple search, an agent needs a plan. Planning is the process of breaking a large, ambitious goal into smaller, manageable steps. Two common strategies for this are Chain-of-Thought and Task Decomposition.

Chain-of-Thought (CoT) is the simplest form of planning. You instruct the model to "think step-by-step." This encourages the LLM to create a basic reasoning trace before giving a final answer. It's an effective way to improve performance on logic puzzles or multi-step math problems without needing a complex agentic loop.

Task Decomposition is a more structured approach. The agent's first step is to create an explicit plan of sub-tasks. For example, if asked to "Plan a 3-day trip to Paris," the agent would first generate a plan like:

1. Research flights and accommodation.
2. Create a daily itinerary with key landmarks.
3. Find restaurant recommendations near each location.
4. Compile all information into a final document.

Only after creating this plan does the agent begin executing the first sub-task, using the ReAct loop for each one.

A crucial part of the agentic loop is reflection. After an agent acts and observes the outcome, it doesn't just blindly move to the next step in its plan. It reflects on the result.

Did the action succeed? Did it produce the expected output? Does this new information change the overall plan? This self-critique allows the agent to be more robust. If a tool fails or an API returns an error, a reflective agent can recognize the failure, think about why it happened, and try a different approach. For example, if a search for best Italian restaurants returns no results, the agent might reflect and try a broader query like restaurants in Little Italy.

This ability to plan, act, and reflect separates a true agent from a simple automation script. It moves the system from merely executing commands to intelligently pursuing a goal.

Let's test your understanding of these core concepts.

By moving beyond linear chains and embracing iterative loops of reasoning, action, and reflection, we can build AI systems capable of tackling truly complex and dynamic problems.