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3 core protocols behind AI agents. I’ve explained each one in simple steps. 1. 𝗠𝗖𝗣 (𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹) • 𝗠𝗮𝗶𝗻 𝗣𝘂𝗿𝗽𝗼𝘀𝗲: It helps AI models (LLMs) use extra information by connecting them to tools or databases. • 𝗦𝗰𝗼𝗽𝗲: Direct connection between tools and the AI model. • 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: Works like a client–server system → server has the tools, model requests them. • 𝗧𝗿𝗮𝗻𝘀𝗽𝗼𝗿𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹: Communicates using normal web messages (HTTP + JSON). • 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 𝗠𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺: Server keeps a list of all tools the model can use. • 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗠𝗼𝗱𝗲𝗹: Only specific APIs are allowed, protected with authentication (OAuth2). • 𝗕𝗲𝘀𝘁 𝗳𝗼𝗿: Apps where AI needs outside data/services. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: ChatGPT pulling data from your Google Sheets or company database. _______________________________________________ 2. 𝗔𝗖𝗣 (𝗔𝗴𝗲𝗻𝘁 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹) • 𝗠𝗮𝗶𝗻 𝗣𝘂𝗿𝗽𝗼𝘀𝗲: Lets multiple agents on the same device work together smoothly. • 𝗦𝗰𝗼𝗽𝗲: Runs locally, focused on the device itself. • 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: No central server; agents talk directly in a local network. • 𝗧𝗿𝗮𝗻𝘀𝗽𝗼𝗿𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹: Uses lightweight channels like IPC, ZeroMQ, or gRPC. • 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 𝗠𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺: Agents announce themselves locally so others can find them. • 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗠𝗼𝗱𝗲𝗹: Runs in a sandbox for safety. • 𝗕𝗲𝘀𝘁 𝗳𝗼𝗿: Edge, embedded, or offline AI (like IoT devices). 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: A set of AI agents on your phone managing emails, calendar, and tasks together. _______________________________________________ 3. 𝗔2𝗔 (𝗔𝗴𝗲𝗻𝘁-𝘁𝗼-𝗔𝗴𝗲𝗻𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹) • 𝗠𝗮𝗶𝗻 𝗣𝘂𝗿𝗽𝗼𝘀𝗲: Lets agents from different platforms or companies communicate. • 𝗦𝗰𝗼𝗽𝗲: Focuses on agents collaborating with each other across systems. • 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: Uses the web (HTTP) with “Agent Cards” that describe what each agent can do. • 𝗧𝗿𝗮𝗻𝘀𝗽𝗼𝗿𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹: JSON-RPC 2.0 over HTTPS (a standard way for web services to talk). • 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 𝗠𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺: Agents share their “cards” so others know who they are and what they can do. • 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗠𝗼𝗱𝗲𝗹: OAuth2 ensures only authorized access, with limited scope for safety. • 𝗕𝗲𝘀𝘁 𝗳𝗼𝗿: Big enterprise workflows linking many agents. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: Two companies’ AI agents sharing info and working together on a project. In short: • 𝗠𝗖𝗣 → 𝗔𝗜 𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝘀 𝘁𝗼 𝘁𝗼𝗼𝗹𝘀 • 𝗔𝗖𝗣 → 𝗔𝗴𝗲𝗻𝘁𝘀 𝘁𝗲𝗮𝗺 𝘂𝗽 𝗹𝗼𝗰𝗮𝗹𝗹𝘆 • 𝗔2𝗔 → 𝗔𝗴𝗲𝗻𝘁𝘀 𝗰𝗼𝗼𝗽𝗲𝗿𝗮𝘁𝗲 𝗮𝗰𝗿𝗼𝘀𝘀 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀 ✅ Repost for others in your network who can benefit from this.