AI Agents

From Chatbots to Agents: The Big Leap

When you ask ChatGPT or Claude a question, they answer — but they don't actually do anything. You have to tell them each step manually.

AI agents are fundamentally different. Give an agent a goal, and it:

Plans — figures out what steps are needed
Uses tools — web search, code execution, API calls, file access
Checks results — verifies whether the task was done correctly
Self-corrects — tries again if something went wrong

Think of it this way: a chatbot is like calling someone for information. An agent is like hiring someone to "get this done" — and they handle every step and report back with results.

How Agents Work: The ReAct Loop

Most AI agents follow the ReAct (Reasoning + Acting) pattern:

Think: "What do I need to do? Which tool should I use?"
Act: Call a tool or run code.
Observe: Check the result — did it work?
Repeat: If needed, go back to step 1.

Core Components of an Agent

LLM (The brain): For thinking and planning — GPT-4, Claude, Gemini, etc.
Tools (Hands and feet): For taking action — web search, code execution, file system, API calls.
Memory: For remembering previous steps and results.
Planning: The ability to break complex tasks into smaller steps.

Real-World Agent Examples

Claude Code: Reads, writes, debugs code, and runs tests.
Devin: A software engineering agent that can build entire features.
AutoGPT: An early general-purpose agent for various autonomous tasks.
Computer Use agents: Can see the screen and use mouse and keyboard like a human.

Multi-Agent Systems

For complex tasks, multiple specialized agents can work together. Just like a software team has a product manager, developer, and tester, a multi-agent system can have a research agent, coding agent, review agent, and more.

A Simple Agent Loop (Python Pseudocode)

# Basic ReAct agent loop structure
def agent_loop(task: str, tools: list, max_steps: int = 10):
    messages = [{"role": "user", "content": task}]
    
    for step in range(max_steps):
        # 1. Think: Ask the LLM what to do
        response = llm.chat(messages, tools=tools)
        
        # 2. If it's the final answer, stop
        if response.is_final_answer:
            return response.content
        
        # 3. Act: Execute the tool call
        tool_result = execute_tool(
            response.tool_name,
            response.tool_args
        )
        
        # 4. Observe: Add result to messages
        messages.append({"role": "tool", "content": tool_result})
        # (Loop continues — agent thinks about what to do next)
    
    return "Max steps reached"

Output