I Built My First AI Agent in Under 2 Hours - Here's How

I've been a software developer for over 20 years. I've seen technologies come and go. But nothing has changed my workflow like AI assistants.

This weekend, I built a fully functional AI research agent with a web interface. It searches the web in real-time, synthesizes information, and presents answers with citations.

Total time: under 2 hours.

What I Built

A research agent that:

• Takes any question


• Searches the web using Tavily API


• Uses Claude to analyze and synthesize the results
  


• Returns a well-organized answer with source citations


• Runs in a clean web interface

I asked it for tomorrow's weather forecast in my city. It searched, found current data, and gave me an accurate answer. That's not a parlor trick - that's a useful tool.

The Tech Stack

• 
  Python - The language


• 
  Anthropic Claude API - The "brain" that reasons and decides what to search


• 
  Tavily API - Web search built for AI agents


• 
  Streamlit - Turned it into a web app with minimal code

Total cost to experiment: about $5-10 in API credits.

The Core Concept: The Agent Loop

The most important thing I learned is how AI agents actually work. It's a simple loop:

1. Receive a task

2. Think: "What do I need to find out?"

3. Use a tool (web search)

4. Look at the results

5. Think: "Do I have enough info?"

6. If no, go back to step 3

7. If yes, synthesize and respond

That's it. This pattern is the foundation of most AI agents, from simple research bots to complex autonomous systems.

What Surprised Me

How fast it came together. I expected this to take weeks of research - finding the right APIs, figuring out how to connect them, debugging integration issues. Instead, with AI assistance, I went from zero to working prototype in a single session.

The system prompt matters. My first version had a bug: when I asked about weather, the agent searched the web but then told me it couldn't search the web. The AI forgot it had tools. A clearer system prompt fixed it. Lesson learned: how you instruct the AI determines how well it performs.

This is accessible now. You don't need a PhD in machine learning. You don't need to train models. With APIs and some Python knowledge, you can build real AI-powered tools today.

The Code Structure

The entire agent is about 150 lines of Python:

# Define tools the agent can use

tools = [

    {

        "name": "web_search",

        "description": "Search the web for current information...",

        "input_schema": {...}

    }

]



# The agent loop

while True:

    response = client.messages.create(

        model="claude-sonnet-4-20250514",

        tools=tools,

        messages=messages

    )



    if response.stop_reason == "end_turn":

        return final_response



    if response.stop_reason == "tool_use":

        # Execute the tool, add results, continue loop

        ...

The AI decides when to use tools, what to search for, and when it has enough information. You just provide the tools and let it reason.

What's Next

This is just the starting point. From here, I could:

• Add more tools (calculator, file reading, API calls)


• Specialize it for a specific domain


• Deploy it for others to use


• Build it into other applications

I'm documenting my journey learning AI development. More posts to come.

Try It Yourself

If you want to build this yourself:

1. Get API keys from Anthropic and Tavily
   


2. Install: pip install anthropic tavily-python streamlit python-dotenv
   


3. The core agent loop is ~100 lines of Python

The barrier to entry has never been lower. The question isn't whether you can build AI tools - it's what you'll build.

This is the first post in a series documenting my experiments with AI agents and automation. Follow along for more.