Home/Agentic AI/First Agent Demo/Agent Architecture

Build Your First AI Agent

Hands-on tutorial: Create a working AI agent from scratch in 15 minutes

Your Progress

0 / 5 completed
1
2
3
4
5

Agent Architecture

Before writing code, let's understand the architecture. Every AI agent follows the same pattern: a continuous loop of thinking, acting, and learning from results.

🔄 The Agent Loop (Interactive)

Click through each step to see how agents process tasks:

📥
Step 1 of 5

Receive Input

User provides a query or task

Example
"What's the weather in Paris today?"

� Agent Code Structure

Here's what our agent code will look like (we'll build this in the next section):

# 1. Initialize the LLM (Brain)
llm = ChatOpenAI(model="gpt-4")

# 2. Define Tools (Actions)
tools = [
    weather_tool,
    calculator_tool,
    search_tool
]

# 3. Create the Agent
agent = initialize_agent(
    tools=tools,
    llm=llm,
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION
)

# 4. Run the Agent
result = agent.run("What's the weather in Paris?")

That's it! ~15 lines of code for a fully functional AI agent.

🆚 Agent vs Traditional Code

❌ Traditional Approach

  • •Write if/else logic for every case
  • •Hard-code API calls and data parsing
  • •Handle error cases explicitly
  • •Update code for new capabilities
  • •Result: 100s of lines of brittle code

✅ Agent Approach

  • •Agent decides logic dynamically
  • •Automatically chooses right tools
  • •Self-corrects when tools fail
  • •Just add new tools to extend
  • •Result: ~15 lines of flexible code

🎯 Key Architecture Decisions

1. Choose Your LLM

GPT-4 (best reasoning), GPT-3.5 (faster/cheaper), Claude (long context), or local models (privacy)

2. Define Tools

Start with 2-3 essential tools. You can always add more later. Each tool needs a name, description, and function.

3. Select Agent Type

ReAct (most common), Plan-and-Execute (complex tasks), or Conversational (chat apps)

4. Add Memory

ConversationBufferMemory (simple), Summary Memory (long chats), or Vector Memory (semantic search)

🌟 What Makes a Good Agent?

🎯

Clear Purpose

Focused on specific tasks, not trying to do everything

🔧

Right Tools

Well-described tools with clear input/output contracts

🧠

Good Prompts

Clear instructions and examples for the LLM