Whether it’s existing consulting firms, new ones that emerge, FDEs from agent vendors, or new internal agent engineering roles, the amount of work that is going to be created to implement agents in enterprises will exceed anything we imagine today.
The complexity of implementing agents in any existing organizations is very real. When I talk to large enterprises, as you move from a chat paradigm to agents that participate in meaningful workflows, there are a number of things they need to do.
First, you have to get agents to be able to talk to your data securely across your systems. In many cases, enterprises have decades of legacy infrastructure that contain the valuable context for AI agents. That’s going to take a ton of work to go modernize and move to systems that work well with agents.
Then, you need to ensure that you’ve implemented agents with the right access controls and entitlements, the right scopes to be safely used, and have ways of monitoring, logging, and securing the work that they do.
Next, you need to actually document the processes in the organization in a way that agents can utilize for doing the work. You also need to figure out what the new workflow looks like when agents and people are working together on a process, and who steps in where. Just replicating the old workflow will mute the gains. Oh and you likely need to create evals for your top new end-state processes.
Finally, you have to keep up with a rapidly changing set of best practices and architectural shifts happening in the agent space. While it’s fun for people to change their personal productivity tools on a dime, it’s 100X harder to do this in a business process. The speed of change is a blessing and a curse right now for anyone trying to keep a stable system design.
All of this means that individuals and companies that develop expertise on the above set of components (and more) are going to be needed to help organizations actually implement agents at scale. This is also the rationale for vertical AI agents right now that can go in deep on a business domain and help bring automation to it.
This is a huge opportunity right now whether you’re doing this internally or as an external business provider.
我在搭建一个 AI 的第二大脑。
目标就是让 AI 更认识我,希望能让 AI 成为一个真正的第二大脑
目前做了几个事。
第一,是把我之前所有的输出,包括文章、笔记,全部都给到 AI,并且结构化,成一个 Obsidian 的 vault让 AI 能够去读。总结我的一些思考方式、观点、思维盲区。
第二,就是我把我所有的跟 AI 的讨论、思考、观点和看的内容,都汇总在 Obsidian 里面,比如说我会有一个 save 的快捷操作,每次 save 之后,AI 就会把这一次的讨论总结放在 obsidian 里面。
以上两个就是一个完整的第二大脑数据库
第三,我让 AI 每周 diff 一次。这样的话,AI 就能知道我最近在思考什么。并且能够针对性的给到意见,后续每季度或者每半年会 review 一下,看一下我这半年都在思考哪些问题,有哪些思维方式的盲区。
先实践半年,半年之后再来看看效果