For twenty years, productivity software has tried to cut down how often we switch between apps. AI agents might change the problem instead of solving it.
An agent is an AI that does a job instead of just answering a question. You ask it to research a topic, sort a folder of files, or fix a bug, and it works through the steps on its own, using a browser, your files, and other apps along the way. ChatGPT, Claude, Gemini, and Copilot all now offer some version of this.
So the interesting question for 2027 isn’t whether we will use fewer apps. It’s what happens to everything in between them.
More of the clicking, copying, and first drafts will move to AI agents. Your part shifts toward deciding what to hand off, giving the agent the right material, checking what comes back, and carrying the good parts into the next task. The desktop becomes less a place where you do every step yourself and more a place where you direct the work and keep what matters close.
We are moving from asking AI to handing it the job
Chat gave us answers. Agents give us finished work, or at least a first version of it. OpenAI put the difference neatly in an August 2026 report: “Assistants help people think through work; agents help them complete it.” Instead of asking an AI how to build a presentation, you ask an agent to gather the material and draft the slides. (Source: OpenAI enterprise report.)
Agents started with coding. The fastest growth is now outside it.
Your job shifts to directing the work
You don’t disappear from the process. The shape of your attention changes: fewer clicks, more decisions.
What’s left for you Choosing the direction, and remembering why.
What’s left for you Knowing what “fixed” looks like, and checking it.
The early data backs this up. In Microsoft’s 2026 Work Trend Index, 86% of AI users said they treat what AI gives them as a starting point rather than a final answer. The most advanced users were also more likely to stop before starting a task and decide which parts the AI should handle and which need their own judgment (53% versus 33%). (Source: Microsoft Work Trend Index 2026.)
The scarce thing becomes good context
An agent can only act on what it is given. Anthropic’s engineering team describes context as a limited resource for agents, something to choose carefully rather than pile on. (Source: Anthropic engineering blog.) I wrote about what happens when you pile it on anyway in context rot, and about the human side of it in context engineering for humans.
People need context too. On any given afternoon, your working context might include:
- The source you want the agent to trust.
- A screenshot it should work from.
- The good version from its last attempt.
- Feedback from a colleague.
- A file you are about to hand to another tool.
- A note on why you rejected an option.
The desktop becomes a staging ground
Not everything needs to live inside the AI chat. Not everything belongs in a project folder yet. Some material is still in motion.
An agent gives you something useful. You keep it. You pull a screenshot from the browser. You keep that too. You reject two drafts but hold on to the third. Then you move the useful parts into the next task, often a different app or a different agent. I wrote about this in-between material before, in the output is easier, the carrying is harder. Agents make the carrying matter more, because each handoff is a chance to lose something.
Keeping work on your own computer matters more
Computers themselves are changing too. Microsoft now promotes “AI PCs” that run some AI features directly on the machine, and its own guide lists the reasons: faster responses, data that stays on your device, and features that keep working offline. (Source: Microsoft AI PC guide.)
As more AI can see your screen and open your files, I think people will become choosier about what they let it see. The habit worth building is simple: keep your working material on your own computer, and hand an agent only what the job needs.
A reality check
Not every agent project will work out. Gartner predicts that over 40% of company agent projects will be canceled by the end of 2027, because of rising costs, unclear value, or weak safeguards. It also warns about “agent washing,” where older chatbots and automation tools get relabeled as agents, and it estimates that only about 130 of the thousands of companies selling agents offer the real thing. (Source: Gartner press release, June 2025.)
That fits what I see day to day. Agents are useful, and they still need a person who knows what good looks like. If anything, that makes the human side of the handoff more important.
Before you hand a job to an agent
- Is the goal one clear sentence? Why: an agent can take many steps in the wrong direction before you notice.
- Did I give it the source to trust? Why: without one, it picks its own, and you inherit its choice.
- Did I include an example of good? Why: one example says more than a paragraph of instructions.
- Did I say what it must not touch? Why: agents act, so a missing limit becomes a real change.
- Do I know how I’ll check the result? Why: agent work can look finished and still be wrong.
- Where will I keep the good parts? Why: the next task, or the next agent, will need them.
Where Tansei fits
Agents will take on more of the steps. The carrying stays with you, and that is why I built Tansei.
Tansei is not an AI agent, and it doesn’t decide what your agent should know. It is a simple shelf for Mac and Windows that sits at the edge of your screen and keeps the material you are moving between apps and AI tools: screenshots, links, files, text, code, colors, and notes. Keep the brief, the good draft, and the reference you are about to hand off, then drag them straight into whichever app or agent needs them next.
Everything stays on your computer, it works offline, and there is no account. The tools doing the work will keep changing. You still need somewhere for the pieces you are working with.
Get Tansei for Mac and Windows
Frequently asked questions
What is an AI agent?
An AI agent is an AI that carries out a job instead of only answering a question. You give it a goal, and it works through the steps itself, using tools like a browser, your files, or other apps, then hands back the result for you to check.
What is an agentic workflow?
An agentic workflow is a way of working where an AI plans and carries out several steps toward a goal, rather than responding to one prompt at a time. A person sets the goal, supplies the context, reviews the result, and decides what happens next.
How will AI agents change productivity in 2027?
More of the hands-on steps, like searching, copying, formatting, and first drafts, will move to agents. The human side shifts toward deciding what to delegate, giving the agent good material, checking its work, and carrying the useful parts into the next task.
Will AI agents replace productivity apps?
Probably not. Apps increasingly become the places agents do their work, while people still need simple ways to review, control, and organize what the agents produce. Some screens you use today will matter less, and the tools for checking and keeping work will matter more.
Why does context matter to AI agents?
An agent can only act on what it is given. The right brief, the source it should trust, examples of good work, and the rules it must follow all shape the result. Missing or messy context is a common reason an agent goes off track.
How does Tansei fit into an AI agent workflow?
Tansei is a simple shelf for Mac and Windows that keeps the material you are carrying between apps and AI tools: screenshots, links, files, text, code, colors, and notes. It is not an agent. It is the place you keep the brief, the good output, and the reference you are about to hand to the next tool. Everything stays on your computer, with no account.
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