(may 3, 2025)

AI Agents act in a loop of reasoning, tool usage, and reflection. The process follows the cycle: Thought → Action → Observation.

🔑 Key Components Explained

Thought

• 💡 What it is: The LLM inside the agent thinks about the user’s request and plans what to do next.
• 🌏 Analogy: Like a chess player planning their next move based on the current board, the agent reasons through the situation to decide on the best step.

Action

• 💡 What it is: Based on its plan generated by the LLM, the agent uses a tool by invoking it with specific arguments.
• 🌏 Analogy: Just as a chess player executes a move after deciding on a strategy, the agent acts by calling the appropriate tool.

Observation

• 💡 What it is: After taking action, the agent receives feedback (e.g., tool output) and reflects on it to decide the next step.
• 🌏 Analogy: After a move in chess, the opponent responds — prompting you another round of thinking. Similarly, the agent reacts to feedback by updating its reasoning.

The TAO cycle repeats until the agent’s goal is achieved. Conceptually, this is like a while loop in programming:

while not goal_finished:
    Thought → Action → Observation

🧠 Understanding “Thought”

In the TAO cycle, Thought refers to the agent’s internal reasoning phase: