The Productisation of AI Agents
AI Agents won’t be a team you manage, they’ll be the products you use.
Forget the fantasy of commanding a personal army of autonomous AI agents. The future of agentic AI won’t look like managing a swarm of personal interns. It’ll look a lot like the products we use today, just a little more helpful. But that small increase in helpfulness will deliver compounding returns.
We’re seeing two worldviews take shape:
Agent Teams vs. Agentic Products.
The Agent Teams worldview imagines individuals directing fleets of AI assistants—delegating tasks, chaining prompts, managing priorities like a one-person company, the CEO of your life.
The Agentic Products worldview is simpler. Agency to decide and act doesn’t show up as a new interface. It shows up as better behavior inside the tools we already use. Not a control panel. Not a prompt box. Just smarter calendars, smarter inboxes, smarter finances.
One model treats AI as something you manage. The other treats it as something that manages with you.
Back when the web was young, the dream was gloriously democratic: everyone would have their own website. A hand-coded corner of the internet, fully owned, fully yours.
In reality? The web eventually centralised around platforms like Facebook, YouTube, Medium, and others that provided experience-layer abstraction—people didn’t want to be webmasters; they wanted to post photos and connect.
As they abstracted away technical complexity, they also shifted control and agency to a few dominant platforms.
The same dream of democratised empowerment is being laid out with agentic AI.
The dominant narrative—especially among technologists and early adopters—is that each of us will command a fleet of intelligent agents. These agents will write your emails, book your travel, manage your finances, even make financial decisions for you.
It’s a compelling vision: AI as a personal team. Always on. Always optimising. You become a kind of CEO of your own life—delegating like a boss, running a one-person company with infinite interns.
It sounds futuristic. It sounds empowering.
It also sounds like a lot of work.
Most People Don’t Want to Be Managers
If you’ve ever managed a team—real or virtual—you know it’s not glamorous. It’s a cycle of clarification, alignment, coaching, and occasional firefighting.
Now imagine doing that with brittle bots that hallucinate, misinterpret, and don’t understand context unless you spoon-feed it.
Managing AI agents doesn’t eliminate complexity. It shifts it—into workflows, debugging, goal-specification, and prompt engineering.
It’s one thing to say:
“Book me a flight to Lisbon.”
It’s another to say:
“Make sure it’s under £700, avoids red-eyes, doesn’t clash with my daughter’s recital, uses my travel points, prefers aisle seats, and leaves me time to prepare for the investor call I haven’t told you about yet.”
And it’s not just the prompting—it’s the meta-work. Knowing what to ask, checking what was done, interpreting what went wrong.
Then, when something goes wrong, you’re not just the boss—you’re also IT support.
The Hidden Cost of Control
Even the best-case version of agent teams—where you’re in charge, issuing high-level commands to your AI minions—has a problem: it assumes you’re always ready to explain yourself.
You have to articulate goals (often vague).
Clarify preferences (often implicit).
Interpret system behaviour (often ambiguous).
Debug failures (often silent).
What looks like autonomy from the outside is actually meta-work on the inside.
To manage agents well, users must:
Be articulate and self aware.
Validate and course-correct alignment with goals and outcomes.
Anticipate or intervene in key decisions.
Understand causality from partial signals to refine and iterate.
Which starts to sound less like delegation—and more like ops.
In theory, you’re the CEO. In practice, you’re middle management.
Agency Shouldn’t be Something to Manage, But Something You Feel
The deeper problem isn’t technical. It’s cognitive.
The idea of agent teams projects the structure of enterprise workflows onto everyday life. Just as companies coordinate teams to get things done, we imagine individuals coordinating fleets of agents.
But most people don’t want to be project managers for their own lives. They don’t want to write SOPs for how to schedule a haircut.
They just want the haircut booked.
I’d rather not start every day with a Scrum standup checking in on what all my bots have been up to while I was asleep. I’d prefer to open my email app and see it’s already drafted an outline of key points to include in my reply to a message I received in the night—I just need to choose a tone of voice to write it in.
The likely reality isn’t a thousand people managing a thousand AI agents. It’s a billion people using familiar products—email apps, calendars, shopping tools—that quietly integrate agentic intelligence under the hood.
We’re not heading toward a world of orchestrated agents. We’re heading toward a world of opinionated, outcome-focused products that happen to be agentic.
From Possibilities to Products
As technology advances, it doesn’t just scale. It simplifies.
We start with open-ended tools—powerful, flexible, and full of potential. But as they become widespread, they shed options in favour of outcomes.
Early computers booted to a blinking cursor. Then came GUIs.
Photoshop gave you every pixel. Canva gives you templates.
Blogs gave you a platform. Twitter gave you a character count.
With each shift, power didn’t disappear. It just got embedded in defaults—in smart assumptions, presets, and structured flows.
AI is following the same arc.
Right now, AI still feels like a command line—flexible, but demanding. But that’s not how most people want to interact.
They don’t want infinite control. They want their needs anticipated.
In the past, the way software anticipated needs was via the choices of software teams selecting which features or controls to include. But with agentic products, those decisions aren’t necessarily made by the teams that create the products, they’re made by the software itself.
The Experience Layer Is the Real Disruption
The real disruption of agentic AI won’t come from creating new types of interfaces for agents you boss around like employees. It’ll come from upgrading the products we already use—quietly, specifically, and in ways that compound over time.
Not through assistants we command, but through experiences that adapt.
Not through generalised intelligence, but through deeply contextual help.
Not through fleets of agents, but through products that are just smart enough to take the next right step.
This is where agentic products outperform agent teams—not just in usability, but in alignment.
Because every product is already a container for user intent.
We open a calendar to plan.
We open a notes app to think.
We open a travel site to go somewhere.
These actions come preloaded with context. We don’t have to explain ourselves from scratch. We don’t need to set goals or issue commands. The product can already know what kind of help is appropriate—because its interface, scope, and affordances are designed around a single use case.
That’s what makes products such a powerful place to embed agentic capabilities. They constrain scope. They imply intent. And they grow with use.
So instead of designing general-purpose agents and asking users to manage them, we can design purpose-built products that quietly take initiative within the boundaries of trust. That means small, smart actions:
Reordering a calendar based on inferred focus patterns.
Summarizing an email thread before you open it.
Suggesting a travel itinerary before you even search.
These aren’t big leaps. They’re tiny optimisations, grounded in the product’s native purpose. But they compound—saving time, reducing friction, and building trust, one small decision at a time.
And when they’re done well, they don’t feel like AI. They feel like help.
The Feeling of Being Understood
This is what agentic products make possible—not a revolution in interface, but a shift in experience.
You open your calendar. Am outline for your key tasks and events has been prepped for your approval—gently, respectfully.
You didn’t ask it to. But it saw the shape of your time and proposed a better version.
One that protects your energy. One that remembers how you actually work.
It’s not magic. It’s pattern recognition. It’s feedback over time. It’s quiet alignment.
And it doesn’t require a breakthrough in general intelligence. It just requires a product that knows when to act—and when not to.
Because when help shows up at the right moment, with the right constraint, and just enough initiative, it doesn’t feel like you’re being managed.
It feels like you’re being understood.
But this kind of support doesn’t work by default. It has to be:
Calibrated — sensitive to uncertainty and context.
Accountable — visible in its effects, even if invisible in its methods.
Correctable — open to feedback and responsive when nudged.
Because trust doesn’t come from handing over control. It comes from seeing, again and again, that something understands your preferences, gets it right, and learns when it doesn’t.
That’s what agentic products can feel like. Not the thrill of automation. Just the quiet sense that something’s on your side.
So How Do We Design for That?
If we’re not building interfaces for agent management—but embedding agency into products—then the design challenge shifts.
We don’t need prompts. We need permission.
We don’t need control panels. We need confidence.
This doesn’t mean removing user input entirely. It means designing for trust in the absence of direct instruction—offering actions that feel right, are easy to reverse, and improve over time.
What does that look like?
A calendar that nudges rather than overrides.
A writing tool that drafts without assuming tone.
A shopping app that knows when to ask before it acts.
It’s not about hiding the agent. It’s about shaping the experience the agent creates—using the product’s own boundaries as scaffolding for alignment.
Because when products start acting on our behalf, we won’t judge them by how autonomous they are.
We’ll judge them by how well they understand what matters—and how lightly they hold that understanding.
What Has to Be True for This to Work?
If we want systems that act without being prompted, we have to ask: what makes that feel acceptable? Even desirable?
For this vision to work, a few things have to be true:
The system must understand intent, not just inputs.
Not just what you asked, but what you meant—and when to ask instead of assume.It must know when it’s unsure.
Ambiguity isn’t failure. Pretending to be confident when you’re not is.It must communicate outcomes, not operations.
Users shouldn’t need to know the precise steps taken. They just need to know it worked—or what happens next.It must invite correction without demanding supervision.
Trust isn’t just built on success. It’s built on graceful failure—moments where the system misreads but recovers with humility. It’ll need to pick up on signals and preferences.It must know when to intervene, and when to invite.
Don’t eliminate friction—place it with purpose. Offer a path, not a guess. Friction becomes a signal: “This is a moment that deserves your input.”
It must offer opt-out moments—not just opt-in ones.
When systems get it wrong—or just get in the way—it must be easy to turn them off, undo their actions, or pause them entirely.
It must set smart expectations, not open with blank canvases.
They can’t make users teach the system everything. It can’t take configuring 80 preferences to get value. Instead, give users opinionated starting points that they can adjust later.
And perhaps most importantly:
It must earn trust slowly, then spend it wisely.
Not all at once. Not everywhere. Not on day one. But gradually through small, reversible acts.
The goal isn’t to remove people from the loop. It’s to design loops that people don’t have to manage—but still feel included in.
Because agency, in this context, isn’t about control. It’s about alignment. It’s about seeing your life reflected back at you—not perfectly, but generously.
Is that what people really want?
Maybe not all at once. Maybe not for every task or action. But bit by bit, as friction fades and confidence builds, we might find ourselves letting go—not because we trust blindly, but because the system earned it.
When Not to Abstract Away Agency
There’s a temptation to treat removal of effort as a universal good. If we can remove friction, shouldn’t we?
But agency isn’t always a burden. Sometimes, it’s the point.
There are moments where decision-making is part of the meaning—where being asked is how we stay grounded in who we are, what matters, and where we’re headed.
Abstraction is powerful. But in the wrong context, it can feel:
Presumptuous – when the system acts on insufficient context.
Patronising – when it assumes users need protecting from complexity they actually care about.
Erasing – when it smooths over differences that matter, especially around culture, identity, or values.
Dangerous – when it acts in high-stakes domains without human verification, transparency, or oversight.
Here are a few kinds of moments where full automation might be the wrong move:
Ethical or identity-driven decisions
“Should I attend this event?”
“Do I want to block this contact?”
These aren’t just logistical—they’re laden with context only the user can hold.Emotionally sensitive communications
An AI can draft condolences or breakups. But should it send them?Ambiguous or novel situations
Generalisation fails at the edges. And the edges can be where people feel most vulnerable.Learning and growth experiences
Sometimes friction is how we grow. A tool that always smooths the path may rob users of agency in places where intentional struggle makes us better people.
The job of good product design isn’t to eliminate user involvement—it’s to know when it’s welcome, and when it’s in the way.
Wrapping Up
The future of agentic AI probably won’t look like fleets of autonomous assistants we command.
It’ll look like the apps we already use—calendars, inboxes, writing tools—that quietly get better.
Not all at once. Not everywhere. Just enough to help, a little earlier than expected.
Because most people don’t want to manage agents. They want outcomes.
They want to feel understood. And products—constrained by context, shaped by intent—are uniquely good at offering that kind of understanding.
That’s why the real opportunity isn’t just in building interfaces for managing intelligence. It’s in designing experiences that express it—experiences that respect the user’s intent, operate within familiar boundaries, and improve through use.
We don’t need AI that performs intelligence. We need AI that behaves intelligently—inside the tools we already trust, doing just enough to be useful, and just little enough to feel human.
And if we do this well, we may not notice a dramatic shift.
Just a quiet sense that things are finally working with us.
Not because they’re perfect. But because they’ve learned to listen.