Gen AI — AI Agents

What are AI agents?

Autonomous systems that use LLMs to plan, reason, and take actions.

Agent architecture

User → Agent → Plan → Execute → Observe → Repeat

Basic agent

import openai

client = openai.OpenAI()

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string"}
                }
            }
        }
    }
]

response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Weather in NYC?"}],
    tools=tools
)

Tool execution

import json

def execute_tool(tool_call):
    if tool_call.function.name == "get_weather":
        args = json.loads(tool_call.function.arguments)
        return get_weather(args["location"])

# Process tool calls
for tool_call in response.choices[0].message.tool_calls:
    result = execute_tool(tool_call)
    
    # Feed result back to model
    messages.append({"role": "tool", "content": str(result)})

Agent loop

def agent_loop(user_message):
    messages = [{"role": "user", "content": user_message}]
    
    while True:
        response = client.chat.completions.create(
            model="gpt-4",
            messages=messages,
            tools=tools
        )
        
        if response.choices[0].message.tool_calls:
            # Execute tools and continue
            for tool_call in response.choices[0].message.tool_calls:
                result = execute_tool(tool_call)
                messages.append({"role": "tool", "content": str(result)})
        else:
            # No more tool calls, return final answer
            return response.choices[0].message.content

Mini Practice

1. Create a simple agent
2. Add multiple tools
3. Implement agent loop
4. Test with complex queries

Up Next

Continue with Function Calling - Connecting to APIs.