𝗠𝗖𝗣 plus 𝗔𝟮𝗔, here is how they complement each other 👇

Protocol wars continue to rage, let’s understand how Googles A2A (Agent2Agent) protocol is different from MCP and how they complement each other (read till the end).

𝘔𝘰𝘷𝘪𝘯𝘨 𝘱𝘪𝘦𝘤𝘦𝘴 𝘪𝘯 𝘔𝘊𝘗:

𝟭. MCP Host - Programs using LLMs at the core that want to access data through MCP.

❗️ When combined with A2A, an Agent becomes MCP Host.

𝟮. MCP Client - Clients that maintain 1:1 connections with servers. 𝟯. MCP Server - Lightweight programs that each expose specific capabilities through the standardised Model Context Protocol. 𝟰. Local Data Sources - Your computer’s files, databases, and services that MCP servers can securely access. 𝟱. Remote Data Sources - External systems available over the internet (e.g., through APIs) that MCP servers can connect to.

𝘌𝘯𝘵𝘦𝘳 𝘈2𝘈:

Where MCP falls short, A2A tries to help. In multi-Agent applications where state is not necessarily shared

𝟲. Agents (MCP Hosts) would implement and communicate via A2A protocol, that enables:

➡️ Secure Collaboration. ➡️ Task and State Management. ➡️ User Experience Negotiation. ➡️ Capability discovery - similar to MCP tools.

Learn all of this Hands-On in my End-to-End AI Engineering Bootcamp: https://maven.com/swirl-ai/end-to-end-ai-engineering… . Next Cohort kicking off October 19th!

🎁 Use code YEAREND20 for 20% off - valid for the next two weeks.

𝗛𝗼𝗻𝗲𝘀𝘁 𝘁𝗵𝗼𝘂𝗴𝗵𝘁𝘀:

❗️ Open protocols for Agent communication are important but they are just a piece of the picture, there will be a need for standards that govern all of the existing protocols and other missing pieces. E.g.

❓ How do we standardise tracing and Observability in multi-agent IOA systems? ❓ How do we retain the identity of the running job of an AI Agent instance if the communication standard is not unified in different parts of the pipeline? ❓ …

✅ More on this in my future posts, stay tuned!

Let me know your thoughts in the comments. 👇

End-to-End AI Engineering Bootcamp by Aurimas Griciunas on Maven

Source: https://maven.com/swirl-ai/end-to-end-ai-engineering The End-to-End AI Engineering Bootcampis an 8-week, cohort-based experience designed to turn technical professionals into full-stack AI engineers who can confidently design, build, and deploy production-grade AI systems.

🛠️ What You’ll Build

You’ll develop your owncapstone project-a real-world AI application built sprint by sprint, applying each week’s concept to solve a business-relevant use case. By the end, you’ll present it live on Demo Day, with a working repo and deployed app you can showcase to hiring managers, CTOs, or investors.

🧑‍💻Technologies include:

• LLM APIs (Gemini, Claude, GPT, etc.).
• Vector databases & RAG.
• AI agent libraries (LangChain, LangGraph, ADK, OpenAI Agents SDK).
• Docker, FastAPI, Kubernetes, cloud deployment.
• Observability, evaluation, and performance testing.
• Communication protocols (A2A, MCP).

🧠 How It Works

Each week follows areal engineering sprint:

• **Sprint Lesson (Monday):**Self-paced learning with videos, cheatsheets & reference code.
• **Sprint Review (Tuesday):**Live walkthrough with Aurimas + deep Q&A.
• **Sprint Build Lab (Thursday):**Live coding session to implement sprint features.
• Bonus QnA and Feedback sessions.

🎬 Pre-course that will help you preparehere.