Design Multi-Agent AI Systems Using MCP and A2A
Engineer your own Python-based agentic AI framework with tool use, memory, and multi-agent workflows
Created by Gigi Sayfan
Explore how to engineer your own Python-based agentic AI framework, focusing on tool use, memory integration, and multi-agent collaboration. Gain practical experience building systems that reason, plan, and adapt using MCP and A2A protocols. Develop skills for both experimentation and production deployment.
Packt | Feb 2026 | 536 min
What You Will Learn
You will start by creating a simple agent in Python and gradually add features like tool schemas, memory, and user interfaces. As you progress, you will implement multi-agent collaboration with A2A messaging, explore secure tool invocation, and deploy agents in realistic environments. Each step builds your confidence and technical depth.
Key Features
- Build Python AI agents with tool use and memory from scratch for full transparency
- Design collaborative multi-agent workflows using secure A2A messaging protocols
- Integrate context and memory with MCP to create adaptive, stateful agent frameworks
Target Audience
Perfect for AI engineers, ML practitioners, and software architects who want to build or scale agentic systems with large language models. If you have Python skills and a basic understanding of LLMs, you will gain hands-on experience and deep insights into designing, debugging, and deploying advanced autonomous AI workflows.
