Building AI Agent2Agent Protocol & Model Context Protocol

This course contains the use of artificial intelligence

Disclosure: AI tools were used only to assist in creating the course outline and course thumbnail. All instructional content, explanations, and project walkthroughs were fully created manually by the instructor.

Welcome to Building AI Agent2Agent Protocol & Model Context Protocol course. This is a comprehensive project based course where you will learn how to build AI agents that are able to communicate with each other using A2A protocol and they are also able to interact with external tools using MCP. This course is a perfect combination between Python and agentic AI, making it an ideal opportunity to practice your programming skills while improving your technical knowledge in AI interoperability. In the introduction session, you will learn the basic fundamentals of agent to agent protocol, such as getting to know its use cases, communication elements, and understanding how it works. Then, in the next section, we will start the project by building two agents that are capable of performing web searches, reasoning, and making decisions autonomously. Firstly, we will create an agent card for each agent. These agent cards will enable other agents to discover their identities, understand their skills and capabilities, and determine how to communicate with them through the A2A protocol. Afterward, we will create and design the first agent, a market research agent. This agent will be able to conduct extensive market research by searching the web, gathering relevant information, analyzing market trends, and identifying potential opportunities. After that, we will create and design the second agent, a competitor analysis agent. This agent will be able to identify key competitors, analyze their products and services, evaluate their strengths and weaknesses, and identify unique selling points to differentiate a business from its competitors. These two agents will be able to communicate, exchange information, and delegate tasks to each other through the A2A protocol. They will combine their findings to generate comprehensive market research and competitor analysis reports which will help businesses to identify market opportunities, understand the competitive landscape, and make informed strategic decisions. Then, we will test these two agents by running them simultaneously in separate terminals and observe how they communicate, exchange information, and collaborate through the A2A protocol. Once we are done with the agent to agent protocol project, we will move on to building a multi agent system. In this section, we will build a corporate strategy multi agent system consisting of three specialized agents. The first agent will act as the CEO and orchestrator, coordinating the other agents, evaluating their findings, and making strategic decisions autonomously. The second agent will act as a business strategist, responsible for creating a comprehensive business plan, defining business objectives, and recommending strategies for growth. The third agent will act as a risk analyst, identifying potential business risks, evaluating their potential impact, and recommending mitigation strategies. These three agents will collaborate to develop effective business strategies. Once we are done with the multi agent system, we will move on to model context protocol. In this section, we are going to build an AI database assistant using MCP. The AI agent will be able to interact with SQLite databases, execute SQL queries, add or modify data, retrieve relevant information, and answer questions about the data using natural language. Lastly, at the end of the course, we are going to test the MCP by asking the AI database assistant to add a new inventory record to the SQLite database and retrieve the updated inventory information.

Firstly, before getting into the course, we need to ask this question to ourselves, why should we build agent to agent protocol and model context protocol? Well, here is my answer, agent to agent protocol enables AI agents to collaborate on complex tasks, while MCP connects AI models to external tools like API or database. By implementing these protocols, we will be able to create more advanced AI systems that can collaborate, access external resources, and automate complex tasks with minimal human intervention.

Below are things that you can expect to learn from this course:

• Learn the basic fundamentals of agent2agent protocol

• Learn how to create AI agent cards and define agent capabilities

• Learn how to build AI market research agent and enable agent to agent communication

• Learn how to create autonomous web search functionality

• Learn how to build AI competitor analysis agent and handle retry logic

• Learn how to extract incoming requests and generate competitor analysis report

• Learn how to build agent client and enable agent discovery

• Learn how to implement task delegation and process agent response

• Learn how to run agent2agent protocol

• Learn how to build corporate strategy multi agent system

• Learn how to handle AI agent delegation and create business report

• Learn how to run multi agent system

• Learn the basic fundamentals of model context protocol

• Learn how to build MCP server and create SQLite database

• Learn how to create inventory management system and handle SQL operations

• Learn how to build AI database assistant and process JSON response

• Learn how to connect MCP client to SQLite database

• Learn how to run AI database assistant MCP