Berryxia.AI (@berryxia) “普通开发者别一天整那些高端货！ 掌握这6点+1 就够了，尤其第7个！ 无需深究Transform” — TopicDigg

Skip transformer math to build AI agents in 2026. You just need these 6 (+1) core architectural pillars. 𝟭. 𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹 (𝗠𝗖𝗣) Think "USB-C for AI." One universal standard that lets any agent plug into external tools and data — instead of hand-building an integration for every tool. Anthropic introduced it; the industry adopted it fast. 𝟮. 𝗔𝗴𝗲𝗻𝘁 𝗟𝗼𝗼𝗽𝘀 The engine behind every agent. A cycle of: perceive → think → act → observe → repeat. The agent keeps looping until the task is done, or it decides it's stuck. No loop, no autonomy. 𝟯. 𝗦𝗸𝗶𝗹𝗹𝘀 The agent's job description. MCP handles the connection and tools expose the API, a Skill is the higher-level logic that orchestrates them into a finished outcome. 𝟰. 𝗦𝗶𝗻𝗴𝗹𝗲 𝘃𝘀 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 Two ends of one spectrum. Single-agent: one LLM runs the whole pipeline. Multi-agent: specialized agents split the work, one retrieves, one validates, one writes, trading simplicity for scale. 𝟱. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 RAG with a brain. The agent can route queries to specialized knowledge sources, validate retrieved context, and make dynamic decisions about what information to use. 𝟲. 𝗔𝗴𝗲𝗻𝘁 𝗠𝗲𝗺𝗼𝗿𝘆 Short-term lives in the context window; long-term is pulled on demand from external stores (knowledge bases or vector databases). It's what keeps agents coherent across interactions, and lets them learn from past ones. 𝟳. 𝗛𝘂𝗺𝗮𝗻-𝗶𝗻-𝘁𝗵𝗲-𝗟𝗼𝗼𝗽 (𝗛𝗜𝗧𝗟) The ultimate guardrail. Autonomous loops are powerful, but pure autonomy is dangerous for high-stakes tasks. HITL inserts human checkpoints for approval or correction before critical actions run. Which term would you add? 🤔