Milvus 对智能体分级（L1-L4）的思考直击当前 AI 代理的痛点——用户注意力成为瓶颈，做多任务自动化的团队可以借鉴其「提前注入判断」的优化策略，提升代理吞吐量。
Milvus 将具备技能的 Hermes 智能体归类为 L3 级别，并解释了 L3 智能体的核心缺陷：用户的大脑成为瓶颈，无法并行审查多个任务，频繁切换会话会降低判断力。优化方法是提前将用户的判断框架、偏好和权衡标准注入智能体，使其能自主评估输出，减少用户注意力消耗。但 L3 智能体仍受限于用户提供的判断框架，若用户不成长，规则会过时，这引出了 L4 智能体如何提升用户自身的问题。
𝗪𝗲 𝗰𝗹𝗮𝘀𝘀𝗶𝗳𝘆 𝗛𝗲𝗿𝗺𝗲𝘀 𝘄𝗶𝘁𝗵 𝘀𝗸𝗶𝗹𝗹𝘀 𝗮𝘀 𝗮𝗻 𝗟𝟯. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝘆, 𝗮𝗻𝗱 ...
𝗪𝗲 𝗰𝗹𝗮𝘀𝘀𝗶𝗳𝘆 𝗛𝗲𝗿𝗺𝗲𝘀 𝘄𝗶𝘁𝗵 𝘀𝗸𝗶𝗹𝗹𝘀 𝗮𝘀 𝗮𝗻 𝗟𝟯. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝘆, 𝗮𝗻𝗱 𝗵𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗱𝗼 𝘁𝗼 𝗴𝗲𝘁 𝗺𝗼𝗿𝗲 𝗼𝘂𝘁 𝗼𝗳 𝗶𝘁. We classify agents into four levels. L1 does the work for you. L2 gets better on its own across sessions. L3 applies the judgment you’ve encoded, even when you are not actively supervising it. L4 improves the users' judgment, not just their output. Hermes + skills is already a strong L3 stack: persistent memory, reusable skills, and a layer for carrying identity and preferences across sessions. But it's still L3, and L3 has its drawbacks. 𝗧𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝗱𝗿𝗮𝘄𝗯𝗮𝗰𝗸 𝗼𝗳 𝗟𝟯 𝗮𝗴𝗲𝗻𝘁𝘀 𝗶𝘀 𝘁𝗵𝗮𝘁 𝘆𝗼𝘂𝗿 𝗯𝗿𝗮𝗶𝗻 𝗰𝗮𝗽𝘀 𝘁𝗵𝗲𝗶𝗿 𝗽𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹. They can run many tasks in parallel, but you can't review them that way. Switching between sessions to check each one costs real cognitive effort, and the more you switch, the worse your judgment gets across all of them. 𝗧𝗵𝗲 𝘄𝗮𝘆 𝘁𝗼 𝗴𝗲𝘁 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗼𝘂𝘁 𝗼𝗳 𝗟𝟯 𝗶𝘀 𝘁𝗼 𝗴𝗶𝘃𝗲 𝘁𝗵𝗲 𝗮𝗴𝗲𝗻𝘁 𝘆𝗼𝘂𝗿 𝗷𝘂𝗱𝗴𝗺𝗲𝗻𝘁 𝘂𝗽 𝗳𝗿𝗼𝗻𝘁: 𝘆𝗼𝘂𝗿 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸, 𝘆𝗼𝘂𝗿 𝗽𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀, 𝗵𝗼𝘄 𝘆𝗼𝘂 𝘄𝗲𝗶𝗴𝗵 𝘁𝗿𝗮𝗱𝗲𝗼𝗳𝗳𝘀, 𝘄𝗵𝗮𝘁 𝗴𝗼𝗼𝗱 𝗹𝗼𝗼𝗸𝘀 𝗹𝗶𝗸𝗲. It evaluates its own output against those standards. You stop switching between sessions to make decisions, and the agent's throughput is less bottlenecked by your attention. 𝗟𝟯 𝗮𝗴𝗲𝗻𝘁𝘀 𝘀𝘁𝗶𝗹𝗹 𝗵𝗮𝘃𝗲 𝗮 𝗹𝗶𝗺𝗶𝘁. The agent can only be as good as the judgment you gave it, and that judgment is only as current as the last time you updated it. If you're not growing, your rules go stale, and the agent keeps performing inside a dated framework. That's the L4 problem: how does the agent improve the person using it, not just serve them faster? More on that next. 💬 0 🔄 0 ❤️ 0 👀 45 ⚡