Get oriented to agentic AI systems, the N² integration problem, and why A2A exists.
Introduction · 7 minsTranscript10 entries
Instructor:Welcome to LocalM Tuts. I am Nilay Parikh. This is the Lesson 1 of 16. In the A2A Protocol course. You will build production grade multi-agent AI system from scratch using 6 different agent framework and 3 model providers. All free-tier or local. We have created a companion tutorial website. At tuts.localm.dev/a2a. We recommend following along there for interactive experience. If you are not starting from the playlist, you can. You can find the playlist link in the description below if you are
Instructor:primarily here for code and hands on implementation. The practical session begin in lesson 5 where you start building your first A2A agent server client from scratch. Lesson 8 through 13 cover the 6 different agent frameworks and lesson 14 is more more sort of a capstone where we everything comes together. In a real-life production pipeline that we strongly recommend walking through this foundation session first. They give you context to understand why the course structure is the way it is. So let's get started.
Instructor:Agentic AI is one of the most exciting frontiers in artificial intelligence, but the challenge is that most agents today operate in isolation. They can call tools using MCP, they can access data, but they cannot talk to each other in a standardized way. If you have 3 agents between the 3 different frameworks, connecting them means writing custom integration code for every pair. That is the N-Squared integration problem. 3 agents need 3 integration, Five agents need 10, and 10 agents need 45. Its
Instructor:scales exponentially. Each integration required a custom message format, custom streaming infrastructure, custom authentication. The engineering cost grew exponentially. While the business value grows linearly. A2A — the Agent-to-Agent protocol — solves this. It is an open standard now under the Linux Foundation and that gives agent a common language to discover each other. Delegate the task stream. The result backed. It is built on proven web infrastructure. HTTP, JSON-RPC 2.0,
Instructor:And Server-Sent Events. Nothing exotic but solid. Now you might be thinking how it is different from MCP? MCP, the Model Context Protocol, connects a single model to its tools and data sources. Think of it as a vertical integration. For example, when we provide a problem or context in Visual Studio Code, GitHub Copilot, the model picks up the problem and then the model uses MCP to access local data sources, create files, or ask for additional information. But that happens in a one session with
Instructor:the one model solving a one set of problem. That is vertical integration. A2A is horizontal integration. It connects autonomous agent to each other. Agent may be running in different models, different frameworks, even managed by different team and solving different problems. They are complementary. So let's not consider one is replacement for other. An An agent can use MCP internally to access tools, A2A externally to talk to other agents and we build these exact pattern in lesson 10 and onwards.
Instructor:Here is what you will build in this course. You will create agents with 6 different frameworks: A2A SDK, Microsoft Agent Framework, Google, ADK, LangGraph, CrewAI, OpenAI Agents SDK, and Claude Agent SDK. Each agent will be powered by one of these 4 models discussed earlier. GitHub Models Phi- 4, Azure AI Foundry, Kimi K2 Thinking, Foundry Local Qwen 2.5, whichever you have access to. They are either free or runs locally free. I mean to say in the free tier you have access to free tier if you have
Instructor:not registered to those. Service provided, so no expensive cloud APIs are actually required. Over the 16 lessons, you will go from zero to a complete multi-agent system. Lessons 1 through 3 cover the protocol fundamentals which I recommend. Please continue with this and lesson 4 to 7 — build the first A2A agent, server, client from scratch. Lessons 8 through 13 exploring those frameworks which discussed earlier. Lesson 14 is a capstone. As I said before This is a near-production real life loan approval pipeline.
Instructor:With 6 different orchestrated agents, Human in loop escalation and react dashboard. And the lesson 15 and 16 covers the advanced topic. They do not deep dive, but they cover security, extension, observability and wrap up there for the next steps. By the end of this course, you will have a deep understanding of the A2A protocol and hands-on experience Building real life production ready multi agent system. So let's get started. Thank you for watching this lesson on LocalM Tuts. In the next lesson, we
Instructor:will explore why A2A exists and the interoperability problem, the five design values, and where A2A fits in the agent stack. You can find the next video in the A2A Protocol course playlist, link on the website, visible below. See you there.
Learning Objectives4
Understand what agentic AI systems are and why they need protocols
Grasp the N² integration problem that A2A solves
Preview the six frameworks and three model providers used in this course
Differentiate A2A (agent-to-agent) from MCP (agent-to-tool)
Q&A
Q & A
Q
What is the N² problem in multi-agent systems?
Without a shared protocol, connecting N agents requires up to N×(N-1)/2 custom integrations. A2A reduces this to one standard interface per agent.
Q
How is A2A different from MCP?
MCP connects a single model to tools and data sources (vertical). A2A connects autonomous agents to each other (horizontal). They are complementary — an agent uses MCP for its tools and A2A to talk to other agents.
Q
Do I need cloud accounts for this course?
No. GitHub Models offers free Phi-4 access with a GitHub account. Azure AI Foundry has free tiers for Kimi-K2. Foundry Local runs Qwen2.5 Coder entirely on your machine with no API key.
Q
What programming language does this course use?
Python 3.11+. All six framework integrations and the A2A SDK are Python-based. The A2A protocol itself is language-agnostic.
