Els vostres clients demanen un nivell de servei més alt?
Un nivell no satisfet és un client perdut, i recuperar-lo serà molt més díficil.
#SolucionsAi4#GestioEficient#Optimitzacio
𝗠𝗖𝗣 𝘃𝘀 𝗔𝗣𝗜.
An 𝗔𝗣𝗜 defines how software systems communicate through specific endpoints, requests, and responses. It gives applications a structured way to access data or trigger functionality in another system.
𝗠𝗖𝗣 gives AI applications a standardized way to discover and use external tools, data, and resources. Instead of building custom integrations for every AI client, an MCP server exposes capabilities through a common protocol.
APIs expose functionality to software. MCP standardizes how AI applications discover and interact with that functionality.
But once AI sits behind an API, the request-response model gets harder.
Inference might take longer than the request can stay open. It might fail halfway through. It might need to be retried.
That changes how the API itself should be designed.
Oracle’s guide breaks down how to design for that with asynchronous jobs, workers, durable state, and predictable API contracts.
𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗴𝘂𝗶𝗱𝗲 → https://t.co/YIJ4uDSgRA
What else would you add?
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🙏 Thanks to @OracleDevs for sponsoring this post.
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Multi-agent systems aren't simply “multiple AI agents working together.”
The architecture you choose determines how agents communicate, share information, and divide work.
Here are 5 useful patterns: