Hosted a dinner last night with a group of IT leaders of large enterprises around agent adoption in the enterprise. Some quick notes: * Change management remains one of the biggest topics for driving workflow transformation. Still most processes need to be upgraded to modern operating models to work with agents, which is a mix of technology, data, and human process change. Lots of emphasis on getting data (structured and unstructured) into a setup that can work with agents properly. * IT teams are finding increasing success embedding full engineers into the business functions (essentially internal FDE) that go and implement agents into the internal workflows. There’s so much technical work to be done to make agents successful, that they can accelerate months or quarters of failed experiments by having someone technical in the workflow early. * Consensus that the tech function is becoming more important than ever. It’s clear that the business could only expect automation to affect a minority of the business before (e.g. ERP) but now it can impact all of knowledge work. This means IT is becoming a more central role to the workflows across the company. * Workflows are cross functional, and getting agents to work cross functionally is a complicated data modeling and permissions issue. Single users don’t have access to this. Which means you need to have agentic systems take on their own roles and have their own privileges, which is non-trivial given agents can’t keep things secure on their own. * Huge variance in budgets between coding work and the rest of knowledge work. Some companies had a $1,000 a month budget for developers, and others had much higher amounts (like $5,000) that were merely triggers to notify the team vs. block them. Far smaller budgets for non-coding work at the moment. * More companies are building their own multimodel systems for routing workloads by task to frontier and lower cost models. Lots of energy around open weights models, but still more in experimentation instead of at scale usage (some companies can’t due to perceived Chinese issue). * Clear sense that all enterprise software must be headless in the future. Relief that they don’t need to train employees on hundreds of different apps. However, clear frustration with the traditional vendors that don’t play extremely nice (technically or cost wise) with agents in a headless fashion. Huge warning for existing software vendors. * Mythos or mythos level-models are finding more and more sophsiticated security risks. The chaining together of vulnerabilities is what’s novel right now, and companies are coming up with long backlogs of what they need to go patch quickly. Even more discussed, but just a few of the hottest topics.
昨晚我和一群大型企业的 IT 负责人共进晚餐，话题围绕企业中的 agent 采用展开。记几点速记：* 变革管理仍然是推动工作流转型的最大议题之一。大多数流程依然需要升级到现代运营模型，才能与 agent 配合工作，这涉及技术、数据以及人工流程变更的组合。大家非常强调要把数据（结构化和非结构化）整理进一种能够正确支持 agent 的配置中。* IT 团队越来越多地发现，把完整的工程师直接嵌入业务职能部门（本质上是内部 FDE）去在内部工作流中实施 agent，效果正在变好。要让 agent 成功落地，还有大量技术工作要做；如果能在工作流早期就有技术人员参与，就能把原本会失败数月甚至数个季度的实验大幅提速。* 大家的共识是，技术职能部门正变得前所未有地重要。很明显，以前企业只能预期自动化影响业务中的少数部分（例如 ERP），但现在它可以影响全部知识型工作。这意味着 IT 正在成为贯穿全公司的工作流中更核心的角色。* 工作流是跨职能的，而让 agent 跨职能运作，会涉及复杂的数据建模和权限问题。单个用户并不拥有这种访问权限。这意味着你需要让 agentic systems 承担它们自己的角色，并拥有它们自己的权限，而这并不简单，因为 agent 本身无法独立保证安全。* 编码工作与其他知识型工作之间的预算差异极大。有些公司给开发者的月预算是 1,000 美元，而另一些公司则高得多（比如 5,000 美元），而且这些额度更多只是触发通知团队，而不是直接阻止他们。当前非编码工作的预算则小得多。* 越来越多的公司正在构建自己的 multimodel systems，用于按任务把工作负载路由到 frontier 模型和低成本模型。大家对 open weights models 很有热情，但目前仍更多停留在实验阶段，而非大规模使用（有些公司因为感知中的 Chinese issue 无法推进）。* 很明显，未来所有企业软件都必须是 headless 的。大家都松了一口气，因为不需要再培训员工去使用上百个不同的 app。不过，对那些在 headless 方式下无法与 agent 非常顺畅协作的传统厂商，无论在技术上还是成本上，大家也表达了明确的不满。这对现有软件供应商是一个巨大的警告。* Mythos 或 mythos level-models 正在发现越来越复杂的安全风险。当前的新情况在于，漏洞被串联起来利用；各家公司也因此列出了很长的待修补清单，需要尽快打补丁。讨论的内容其实更多，这里只是其中几个最热的话题。
Fantastic to see more open weights innovation happening right now, especially coming from a US Lab. The future of AI is going to be a mix of frontier intelligence that you can use as an orchestrator combined with either lower cost or tuned models for your workhorse tasks.
很高兴看到现在有更多 open weights 创新正在发生，尤其是来自一家 US Lab。AI 的未来将会是这样一种组合：用 frontier intelligence 作为 orchestrator（编排器），再结合更低成本或经过调优的模型，去承担你的主力工作任务。