Demystifying AI Agents: How Language Models Think, Act, and Learn in the Real World

AI agents are the next step in making intelligent systems more interactive, capable, and autonomous. Instead of just answering questions, agents can reason through complex tasks, use tools, interact with their environment, and adapt to feedback. In this blog, we break down the core building blocks of AI agents in simple terms.

🧠 What is an Agent?

An agent is a system that can:

1. Perceive its environment (through inputs like queries or data)
2. Reason or plan its next steps
3. Act by calling external tools or APIs
4. Learn or adapt based on the outcome of its actions

In LLM-powered systems, the agent uses a language model to "think," tools to "act," and observations to improve future decisions.

🧱 What is an LLM?

An LLM (Large Language Model) like GPT-4, Claude, or Gemini is trained on large amounts of text to predict the next token in a sequence. It powers the reasoning, planning, and language generation abilities of an agent.

Think of it as the brain of the agent that understands instructions, generates thoughts, and decides what to do next.

🛠️ Tools: Extending the LLM's Abilities

LLMs are limited by design; they can't access real-time information or perform actions on external systems. That's where tools come in:

Tools are external functions the agent can call to:

• Search the web
• Query a database
• Fetch weather or stock data
• Execute code

Example tool call:

{
  "action": "get_weather",
  "input": "India"
}

💬 Messages and Special Tokens

Agentic systems rely on structured communication using messages and, in some frameworks, special tokens. These help manage conversations, tool usage, and the agent’s internal reasoning.

📬 Message Roles

Each message has a role that defines its purpose:

• system – Sets the agent's behavior or instructions.

_Example: “You are an AI agent that can use tools.”_

• user – The human's or calling app’s input.

_Example: “What’s the weather in Tokyo?”_

• assistant – The LLM's response (thoughts, plans, or final answers).

_Example: “Action: get_weather, Input: Tokyo”_

• tool – The result of a tool call.

_Example: “Observation: It's 27°C and sunny in Tokyo.”_

🧪 Special Tokens

Some frameworks (e.g., OpenAI, LangGraph) use tokens or delimiters to mark parts of the response:

• <|thought|>, <|action|>, <|observation|> – Used to guide parsing
• Ensures the system can stop at the right point and extract actions

🔁 Why It Matters

This structure lets agents:

• Manage multi-turn workflows
• Separate thought from action
• Safely interact with tools

Together, messages and special tokens form the backbone of how agents think, act, and learn step-by-step.

⟳ The Thought → Action → Observation Cycle

This cycle is at the heart of agentic reasoning. The model reasons, acts, observes the result, and thinks again.

🔎 Diagram: Thought-Action-Observation Cycle

This loop continues until the task is complete.

🧬 Thought = Internal Reasoning

Not every step involves an action. Sometimes, the agent just thinks out loud to plan its next move.

These internal thoughts:

• Help break down complex problems
• Allow for step-by-step execution
• Improve transparency

⚛️ The ReAct Approach

ReAct stands for Reasoning + Acting. It’s a popular approach for LLM-based agents.

ReAct Agent Output Example:

User: Convert 10 kilometers to miles.

Thought: I need to convert 10 kilometers to miles.

Action: Call a unit conversion tool.

Observation: 10 kilometers is approximately 6.21 miles.

Response: 10 kilometers is approximately 6.21 miles.

By alternating between reasoning and acting, the agent becomes more accurate and reliable.

🌍 Actions: Interacting with the Environment

Once the model has thought through its strategy, it uses actions to make changes in the world:

• Query APIs
• Execute shell commands
• Send messages
• Retrieve or update records

This is what makes agents actually do things instead of just say things.

👀 Observation: Reflect and React

Every action yields an observation — feedback from the environment.

The agent then:

• Evaluates whether the result met the goal
• Adapts its next thought
• May retry or take alternative actions

This closes the loop and makes agents dynamic and responsive.

✅ Final Thoughts

LLMs become truly powerful when you turn them into agents:

• They can plan and act
• Use tools to bridge gaps
• Think, act, and observe in cycles
• Improve with feedback

You’ve just seen the architecture behind the smartest AI systems today — from coding copilots to research assistants. Whether using LangChain, SmolAgents, or custom frameworks, AI agents are how we move from static chat to autonomous intelligence.