In 𝟮𝟬𝟮𝟰, we saw the technology focus shifting from 𝗖𝗵𝗮𝗶𝗻 𝗼𝗳 𝗧𝗵𝗼𝘂𝗴𝗵𝘁 (𝗖𝗼𝗧) approach to 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚), reflecting the need for precise, contextual responses in generative AI.
Building on 𝗥𝗔𝗚, 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 emerged, adding 𝘢𝘶𝘵𝘰𝘯𝘰𝘮𝘰𝘶𝘴 𝘤𝘢𝘱𝘢𝘣𝘪𝘭𝘪𝘵𝘪𝘦𝘴 for AI to dynamically retrieve, interpret, and act on data.
As these technologies advanced, attention grew around 𝗦𝗺𝗮𝗹𝗹 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 and 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹𝘀, balancing task-specific efficiency with general-purpose adaptability.
This trajectory then accelerated toward 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 capable of more complex, interactive roles, paving the way for something I like to call 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗫 — a framework embedding agentic capabilities directly into applications, making them proactive, adaptive, and contextually aware in meeting user goals independently.