An AI agent is more than just a chatbot that answers a single question. It's a system designed to achieve a goal. To do this, it needs a way to think, act, and learn from what happens. It doesn't just respond; it operates in a continuous loop, much like a person trying to solve a puzzle.

At the heart of this system is a reasoning engine, almost always a large language model (LLM). But instead of using the LLM for a one-off answer, the agent's architecture uses it iteratively. The agent assesses a situation, forms a thought, takes an action, and then observes the outcome. This cycle repeats until the goal is met.

The most fundamental pattern for an agent's brain is the ReAct framework, which stands for "Reason + Act." It's an elegant loop that combines the LLM's reasoning abilities with the power to take action in a digital environment. The process is simple but powerful.

It works in three steps:

1. Reason: The agent uses the LLM to generate an internal thought. This thought analyzes the current situation, considers the overall goal, and decides on the single next best action to take. For example: "To answer the user's question about today's weather in Paris, I need to use the weather tool."
2. Act: Based on the thought, the agent selects and executes a specific action. This usually involves calling a tool, like a web search API, a calculator, or a database query function.
3. Observe: The agent receives the result from the tool—the weather data, the calculation's answer, or the database records. This new information, or "observation," is fed back into the start of the loop.

This cycle continues, with each observation informing the next thought, until the agent determines the final goal has been achieved.

This iterative process allows the agent to correct its course. If a search fails or an action returns an unexpected result, the agent can observe the error, reason about a new approach, and act again. It’s a robust way to handle the unpredictability of real-world tasks.

The ReAct loop works well for taking the next step, but what about complex goals that require many steps? An agent can't just stumble its way to a solution. It needs a plan. This is where task decomposition comes in—breaking a large goal into smaller, manageable sub-tasks.

One of the earliest and most straightforward techniques is Chain-of-Thought (CoT). With CoT, the agent generates a linear, step-by-step plan to solve a problem. Think of it like a recipe: do step 1, then step 2, then step 3. Each step follows logically from the previous one, forming a single, unbroken chain of reasoning. For many problems, this straightforward, linear approach is all that's needed.

But what if the problem has multiple possible paths, dead ends, or requires exploring different options? A simple chain isn't enough. For this, we have a more advanced strategy: Tree-of-Thoughts (ToT). Instead of a single chain, ToT allows the agent to explore multiple reasoning paths at once, like branches on a tree.

At each step, the agent can generate several different 'thoughts' or potential next steps. It can then evaluate these branches, discard the unpromising ones, and continue exploring the most likely paths. This allows the agent to backtrack from a dead end or compare different solutions to find the best one. It's a more deliberate and robust way of thinking that mimics human problem-solving, where we often consider several possibilities before choosing one.

By combining a high-level plan (from CoT or ToT) with the low-level execution loop (ReAct), an agent can tackle sophisticated goals. It first decomposes the problem into a strategic plan, then executes each step of that plan using the reason-act-observe cycle.

Ready to test your understanding of how these agentic architectures work?

These reasoning and planning structures are the fundamental building blocks that allow an AI agent to move from simple instruction-following to autonomous problem-solving.