# Understanding AI Agents through the Thought-Action-Observation Cycle

In the previous sections, we learned:

- **How tools are made available to the agent in the system prompt**.
- **How AI agents are systems that can 'reason', plan, and interact with their environment**.

In this section, **we’ll explore the complete AI Agent Workflow**, a cycle we defined as Thought-Action-Observation. 

And then, we’ll dive deeper into each of these steps.

## The Core Components

Agents' work is a continuous cycle of: **thinking (Thought) → acting (Act) and observing (Observe)**.

Let’s break down these actions together:

1. **Thought**: The LLM part of the Agent decides what the next step should be.
2. **Action:** The agent takes an action by calling the tools with the associated arguments.
3. **Observation:** The model reflects on the response from the tool.

## The Thought-Action-Observation Cycle

The three components work together in a continuous loop. To use an analogy from programming, the agent uses a **while loop**: the loop continues until the objective of the agent has been fulfilled.

Visually, it looks like this:

In many Agent frameworks, **the rules and guidelines are embedded directly into the system prompt**, ensuring that every cycle adheres to a defined logic.

In a simplified version, our system prompt may look like this:

We see here that in the System Message we defined :

- The *Agent's behavior*.
- The *Tools our Agent has access to*, as we described in the previous section.
- The *Thought-Action-Observation Cycle*, that we bake into the LLM instructions.

Let’s take a small example to understand the process before going deeper into each step of the process.

## Alfred, the weather Agent

We created Alfred, the Weather Agent.

A user asks Alfred: “What’s the current weather in New York?”

Alfred’s job is to answer this query using a weather API tool. 

Here’s how the cycle unfolds:

### Thought

**Internal Reasoning:**

Upon receiving the query, Alfred’s internal dialogue might be:

*"The user needs current weather information for New York. I have access to a tool that fetches weather data. First, I need to call the weather API to get up-to-date details."*

This step shows the agent breaking the problem into steps: first, gathering the necessary data.

### Action

**Tool Usage:**

Based on its reasoning and the fact that Alfred knows about a `get_weather` tool, Alfred prepares a JSON-formatted command that calls the weather API tool. For example, its first action could be:

Thought: I need to check the current weather for New York.

 ```
    {
      "action": "get_weather",
      "action_input": {
        "location": "New York"
      }
    }
 ```

Here, the action clearly specifies which tool to call (e.g., get_weather) and what parameter to pass (the “location": “New York”).

### Observation

**Feedback from the Environment:**

After the tool call, Alfred receives an observation. This might be the raw weather data from the API such as:

*"Current weather in New York: partly cloudy, 15°C, 60% humidity."*

This observation is then added to the prompt as additional context. It functions as real-world feedback, confirming whether the action succeeded and providing the needed details.

### Updated thought

**Reflecting:**

With the observation in hand, Alfred updates its internal reasoning:

*"Now that I have the weather data for New York, I can compile an answer for the user."*

### Final Action

Alfred then generates a final response formatted as we told it to:

Thought: I have the weather data now. The current weather in New York is partly cloudy with a temperature of 15°C and 60% humidity."

Final answer : The current weather in New York is partly cloudy with a temperature of 15°C and 60% humidity.

This final action sends the answer back to the user, closing the loop.

What we see in this example:

- **Agents iterate through a loop until the objective is fulfilled:**
    
**Alfred’s process is cyclical**. It starts with a thought, then acts by calling a tool, and finally observes the outcome. If the observation had indicated an error or incomplete data, Alfred could have re-entered the cycle to correct its approach.
    
- **Tool Integration:**

The ability to call a tool (like a weather API) enables Alfred to go **beyond static knowledge and retrieve real-time data**, an essential aspect of many AI Agents.

- **Dynamic Adaptation:**

Each cycle allows the agent to incorporate fresh information (observations) into its reasoning (thought), ensuring that the final answer is well-informed and accurate.
    
This example showcases the core concept behind the *ReAct cycle* (a concept we're going to develop in the next section): **the interplay of Thought, Action, and Observation empowers AI agents to solve complex tasks iteratively**. 

By understanding and applying these principles, you can design agents that not only reason about their tasks but also **effectively utilize external tools to complete them**, all while continuously refining their output based on environmental feedback.

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Let’s now dive deeper into the Thought, Action, Observation as the individual steps of the process.