DEV Community: Thulani Zondo

What is AI Agent Academy? A Complete Guide

Thulani Zondo — Mon, 01 Jun 2026 16:43:08 +0000

What is AI Agent Academy?

Learn to build AI agents from scratch with Python, Claude, LangChain, LlamaIndex, and CrewAI. 34 lessons, verified certificate.

Key Features

AI Agent Academy provides developers and users with powerful capabilities to solve real-world problems efficiently.

Getting Started

Visit AI Agent Academy to get started. The platform is designed to be intuitive and accessible.

Why Choose AI Agent Academy?

Easy to use interface
Powerful features
Available online at https://learnhowtobuildaiagents.com
Regular updates and improvements

Conclusion

AI Agent Academy is a valuable tool for anyone looking to learn to build ai agents from scratch with python, claude, langchain, llamaindex, and crewai. 34 lessons, verified certificate..

👉 Try it free: https://learnhowtobuildaiagents.com

MCP (Model Context Protocol) Explained — The Universal AI Tool Standard

Thulani Zondo — Mon, 01 Jun 2026 15:36:48 +0000

Model Context Protocol (MCP) is an open standard by Anthropic that standardizes how AI models connect to external tools. Think of it as USB for AI.

The Problem MCP Solves

Before MCP, every AI tool integration was custom code. MCP provides one standard protocol for all tools.

Building an MCP Server

from mcp.server import Server
import json

server = Server('my-tools')

@server.tool()
async def search_database(query: str) -> str:
    """Search the product database."""
    results = db.search(query)
    return json.dumps(results)

@server.tool()
async def send_email(to: str, subject: str, body: str) -> str:
    """Send an email."""
    send(to, subject, body)
    return f'Email sent to {to}'

if __name__ == '__main__':
    server.run()

Why MCP Matters

Reusability — Build once, use with any MCP-compatible AI
Ecosystem — Growing library of pre-built MCP servers
Security — Built-in permission model and sandboxing
Composability — Mix tools from different servers

MCP Architecture

AI Model (Client) <--MCP Protocol--> MCP Server (Tools)
                                           |
                                    Database / API / Files

Using MCP with Claude Desktop

Configure in your Claude Desktop settings:

{
  "mcpServers": {
    "my-tools": {
      "command": "python",
      "args": ["my_mcp_server.py"]
    }
  }
}

Learn More

Full lesson on Claude + MCP in our AI agent course:

Start free (no signup needed): learnhowtobuildaiagents.com

Build Your First AI Agent with Claude (Anthropic) — Python Tutorial

Thulani Zondo — Mon, 01 Jun 2026 15:31:57 +0000

Claude is Anthropic's AI model with a 200K token context window and native tool use. Here's how to build an agent with it.

Setup

pip install anthropic

Basic Agent

import anthropic
import json

client = anthropic.Anthropic(api_key="your-key")

tools = [{
    "name": "calculate",
    "description": "Perform mathematical calculations",
    "input_schema": {
        "type": "object",
        "properties": {
            "expression": {"type": "string"}
        },
        "required": ["expression"]
    }
}]

def agent_with_tools(task: str) -> str:
    messages = [{"role": "user", "content": task}]

    while True:
        response = client.messages.create(
            model="claude-sonnet-4-20250514",
            max_tokens=2048,
            tools=tools,
            messages=messages
        )

        if response.stop_reason == "tool_use":
            tool_block = next(b for b in response.content if b.type == "tool_use")
            result = {"result": eval(tool_block.input["expression"])}

            messages.append({"role": "assistant", "content": response.content})
            messages.append({"role": "user", "content": [{
                "type": "tool_result",
                "tool_use_id": tool_block.id,
                "content": json.dumps(result)
            }]})
        else:
            return response.content[0].text

print(agent_with_tools("What is 1547 * 382 + 99?"))

Why Claude for Agents?

200K token context window
Native tool use (no workarounds)
Extended thinking for complex reasoning
Constitutional AI for safety

Learn More

Full course covering Claude, LangChain, LlamaIndex, CrewAI, and MCP:

👉 learnhowtobuildaiagents.com — Module 1 free, no signup needed

What Are AI Agents? A Complete Beginner's Guide (2026)

Thulani Zondo — Mon, 01 Jun 2026 15:31:54 +0000

An AI agent is a software system that can perceive its environment, reason about what it observes, and take autonomous actions to achieve specific goals.

The Agent Loop

Every AI agent follows: Perceive → Reason → Act → Learn

Types of AI Agents

Simple Reflex Agents — React to current input only (like a thermostat)
Model-Based Agents — Maintain internal state about the world
Goal-Based Agents — Plan actions to achieve specific objectives
Learning Agents — Improve performance over time (most modern agents)

Building Your First Agent

import anthropic

client = anthropic.Anthropic(api_key="your-key")

def simple_agent(task: str) -> str:
    response = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=2048,
        system="You are a helpful assistant. Think step by step.",
        messages=[{"role": "user", "content": task}]
    )
    return response.content[0].text

result = simple_agent("What are the 3 most important things to know about RAG?")
print(result)

Real-World Applications

AI agents power: GitHub Copilot, ChatGPT, Claude, autonomous vehicles, trading bots, customer support systems, and DevOps automation.

Learn More

I built a full structured course covering 34 lessons on AI agent development with Claude, LangChain, LlamaIndex, and CrewAI.

👉 Start free (no signup needed for Module 1): learnhowtobuildaiagents.com