Another new idea to push the state of AI architectures forward. Sakana released a model that effectively uses a mixture of models to get work done. You get a single API but then the work gets farmed out the model that best performs the task. “Fugu manages model selection, delegation, verification, and synthesis automatically. It solves tasks directly when that is enough, or coordinates a team of expert models when a problem calls for more. The complexity of a multi-agent system never reaches your code.” This is generally how applied AI products are building their agent harnesses at this point, but the idea of making this an LLM that any developer can interact with is also a great idea. As we get more innovation with both frontier closed and OSS models, there’s going to be a ton of value produced for the layer that can route the best.
又一个推动 AI 架构发展的新想法。Sakana 发布了一个模型，它通过有效使用 mixture of models（模型混合）来完成工作。你得到的是一个单一的 API，但随后工作会被分发给最擅长该任务的模型。“Fugu 会自动管理模型选择、任务委派、验证和综合。当直接解决任务已经足够时，它就直接处理；而当问题需要更多能力时，它会协调一支由专家模型组成的团队。多 agent 系统的复杂性永远不会传达到你的代码中。” 目前，应用型 AI 产品大体上都是这样构建它们的 agent harness（agent 编排/承载层）的，但把这件事做成一个任何开发者都能交互的 LLM，同样是个很棒的想法。随着 frontier closed 和 OSS 模型两端都持续创新，能够把请求路由给最佳模型的那一层将会创造出大量价值。
Agents will use software 100X more than people. When that happens, theres a huge need for guardrails on what the agents are doing so they don’t leak data or change the wrong information, authoritative sources of truth for them to work with, logging and auditing of what they’re doing, the ability to collaborate with people through these systems, and more. A simple query on any given agentic task could pull in more data than a user touches in a month. As a result, there are lots of categories of software that when it goes headless that the usage and value go up substantially. Agents will end up using our CRM data, documents and corporate knowledge, analytics data, and other information far more than people ever did. The platforms that can move toward the model of powering these headless interactions, and have a business model and technology strategy to support this, will be in the best position in the future.
Agents 使用软件的频率将会是人的 100 倍。当这种情况发生时，就会非常需要 guardrails（护栏/约束机制）来限制 agents 在做什么，以防它们泄露数据或修改错误的信息；还需要它们可以依赖的权威 truth source（事实来源），对其行为进行 logging 和 auditing（日志记录与审计），以及通过这些系统与人协作的能力，等等。对任何一个给定的 agentic task（agent 式任务）做一次简单查询，调取的数据量都可能比一个用户一个月接触的数据还多。因此，很多类别的软件一旦变成 headless（无界面/无头）模式，其使用量和价值都会显著提升。Agents 最终会比人更多地使用我们的 CRM 数据、文档和企业知识、分析数据以及其他信息。那些能够转向为这些 headless 交互提供底层能力的平台，并且拥有与之配套的商业模式和技术战略的公司，未来将处于最有利的位置。