You Is All You Need
I think the really valuable personal agent is the one that mimics the most of you. If you want an agent like Copilot to replace you by doing your own work, you need to tune that agent to get closer to you.
That is the idea behind You Is All You Need.
Tools such as GitHub Copilot can already write code, explain a repository, and carry out a list of tasks.1 A general agent can know much more about programming than I do. That does not mean it will naturally do my work the way I want it done.
My work contains many small preferences that are obvious to me but invisible to a new agent. I care about which problem to solve first, how much explanation is useful, when a change is too broad, what evidence is enough, and which tradeoffs I am willing to make. Two implementations can both be technically correct while only one feels like something I would choose.
This is why I think the long-term value of a personal agent comes from getting closer to its user. It should learn from my instructions, tools, corrections, and repeated decisions. If I keep asking it to shorten an explanation, preserve a particular boundary, or verify a claim before writing it down, those corrections should eventually become part of how it works with me.
I have started treating my instructions and workflows as a portable personal layer instead of configuring every agent separately. When I tell an agent to preserve my original wording, avoid em dashes, or verify a change before publishing it, I can save that correction as a reusable workflow. The next agent does not need to learn the same preference from the beginning.
I do not mean that an agent needs to copy my personality or pretend to be me. I mean something more practical: it should understand my working habits well enough that I do not have to restate them in every session. The better it understands those habits, the more useful it becomes for my own work rather than for a generic demo.
I want to take this idea further by building a version of myself, exposing it through my personal website, and eventually offering it as a kind of consultant. It would not claim to be me. It would make the methods, preferences, and knowledge I am willing to share available when I am not there to answer directly.
There is also a limit. I still want the agent to question a bad assumption, find information I missed, and suggest a better path. An exact copy of me would repeat my blind spots. The useful agent should be close enough to understand my intent, but independent enough to improve the result.
So when I think about tuning a personal agent, I am less interested in making it sound like me. I want it to make choices that I recognize, explain the places where it disagrees, and improve through the feedback I already give it. The model matters, but the accumulated understanding between the agent and its user may matter more.
For a personal agent, you may be the most important part of the context.