As a software developer, and my daily workflow has changed completely over the last 1.5 years.

Before, I had to focus for hours on end on a single task, one at a time. Now I am juggling 1 to 5 AI agents in parallel at any given time. I have become an engineering manager for agents.

If you are a knowledge worker who is not using AI agents in such a manner yet, I am living in your future already, and I have news from then.

Most of the rest of your career will be spent on a chat interface.

“The future of AI is not chatbots” some said. “There must be more to it.”

Despite the yearning for complexity, it appears more and more that all work is converging into a chatbot. As a developer, I can type words in a box in Codex or Claude Code to trigger work that consume hours of inference on GPUs, and when come back to it, find a mostly OK, sometimes bad and sometimes exceptional result.

So I hate to be the bearer of bad (or good?) news, but it is chat. It will be some form of chat until the end of your career. And you will be having 1 to 5 chat sessions with AI agents at the same time, on average. That number might increase or decrease based on field and nature of work, but observing me, my colleagues, and people on the internet, 1-5 will be the magic number for the average worker doing the average work.

The reason is of course attention. One can only spread it so thin, before one loses control of things and starts creating slop. The primary knowledge work skill then becomes knowing how to spend attention. When to focus and drill, when to step back and let it do its thing, when to listen in and realize that something doesn’t make sense, etc.

Being a developer of such agents myself, I want to make some predictions about how these things will work technically.

Agents will be created on-demand and be disposed of when they are finished with their task.

In short, on-demand, disposable agents. Each agent session will get its own virtual machine (or container or kubernetes pod), which will host the files and connections that the agent will need.

Agents will have various mechanisms for persistence.

Based on what you want to persist, e.g.

  • Markdown memory, skills or weight changes on the agent itself,
  • or the changes to a body of work coming from the task itself,

agents will use version control including but not limited to git, and various auto file sync protocols.

Speaking of files,

Agents will work with files, like you do.

and

Agents will be using a computer and an operating system, mostly Linux or a similar Unix descendant.

And like all things Linux and cloud,

It will be complicated to set up agent infra for a company, compared to setting up a Mac for example.

This is not to say devops and infra per se will be difficult. No, we will have agents to smoothen that experience.

What is going to be complicated is having someone who knows the stack fully on site, either internal or external IT support, working with managers, to set up what data the agent can and cannot access. At least in the near future. I know this from personal experience, having worked with customers using Sharepoint and Business OneDrive. This aspect is going to create a lot of jobs.

On that note, some also said “OpenClaw is Linux, we need a Mac”, which is completely justified. OpenClaw installs yolo mode by default, and like some Linux distros, it was intentionally made hard to install. This was to prevent the people who don’t know what they are doing from installing it, so that they don’t get their private data exfiltrated.

This proprietary Mac or Windows of personal agents will exist. But is it going to be used by enterprise? Is it going to make big Microsoft bucks?

One might think, looking at 90s Microsoft Windows and Office licenses, and the current M365 SaaS, that enterprise agents will indeed run on proprietary, walled garden software. While doing that, one might miss a crucial observation:

In terms of economics, agents, at least ones used in software development, are closer to the Cloud than they are close to the PC.

It might be a bit hard to see this if you are working with a single agent at a time. But if you imagine the near future where companies will have parallel workloads that resemble “mapreduce but AI”, not always running at regular times, it is easy to understand.

On-site hardware will not be enough for most parallel workloads in the near-future. Sometimes, the demand will surpass 1 to 5 agents per employee. Sometimes, agent count will need to expand 1000x on-demand. So companies will buy compute from data centers. The most important part of the computation, LLM inference, is already being run by OpenAI, Anthropic, AWS, GCP, Azure, Alibaba etc. datacenters. So we are already half-way there.

Then this implies a counterintuitive result. Most people, for a long time, were used to the same operating system at home, and at work: Microsoft Windows. Personal computer and work computer had to have the same interface, because most people have lives and don’t want to learn how to use two separate OSs.

What happens then, when the interface is reduced to a chatbot, an AI that can take over and drive your computer for you, regardless of the local operating system? For me, that means:

There will not be a single company that monopolizes both the personal AND enterprise agent markets, similar to how Microsoft did with Windows.

So whereas a proprietary “OpenClaw but Mac” might take over the personal agent space for the non-technical majority, enterprise agents, like enterprise cloud, will be running on open source agent frameworks.

(And no, this does not mean OpenClaw is going enterprise, I am just writing some observations based on my work at TextCortex)

And I am even doubtful about this future “OpenClaw but Mac” existing in a fully proprietary way. A lot of people want E2E encryption in their private conversations with friends and family, and personal agents have the same level of sensitivity.

So we can definitely say that the market for a personal agent running on local GPUs will exist. Whether that will be cornered by the Linux desktop1, or by Apple or an Apple-like, is still unclear to me.

And whether that local hardware being able to support more than 1 high quality model inference at the same time, is unclear to me. People will be forced to parallelize their workload at work, but whether the 1 to 5 agent pattern reflecting to their personal agent, I think, will depend on the individual. I would do it with local hardware, but I am a developer after all…

  1. Not directly related, but here is a Marc Andreesen white-pill about desktop Linux ↩