There’s something I’ve understood for years, long before AI entered the picture. If you want someone to understand you, the responsibility sits with you, the communicator. It doesn’t matter whether you’re writing requirements, presenting an architecture decision, or explaining why the business can’t have real-time cross-region consistency without paying for it somewhere else. If your audience doesn’t understand, that’s on you. It’s not enough to be technically right; you have to bring people with you.
Now apply that to AI agents.
AI is not perfect. It hallucinates, it misinterprets, it makes assumptions. It’s basically a very fast, very capable junior architect who hasn’t lived your career and doesn’t share your context. You cannot give it a vague prompt and expect a masterpiece. You have to iterate. You have to refine. You have to course-correct. The sooner you accept that you are not a perfect communicator, the sooner you start getting real value from agentic AI.
Once you internalise that, working with AI becomes far easier.
A quick tour of how we got here
Thirty years ago, if you wanted to send an email, you built the email module. If you wanted authentication, you wrote the authentication layer yourself. If you wanted a database connection, you handled the sockets. Everything was bespoke. Everything was code.
Twenty years ago, frameworks started abstracting away the heavy lifting. Fifteen years ago, SDKs, libraries, jQuery, .NET, all of it meant you didn’t need to know the depths of SMTP or SQL drivers. You could focus on solving the problem, not reinventing the wheel.
Then cloud and DevOps came along. Infrastructure turned into products. Deployment pipelines became codified. Observability became a commodity. Developers expanded their cognitive range. “Full stack” stopped being a joke and became an expectation.
Each wave allowed people to go broader because the depth had been abstracted away.
Agentic AI is simply the next wave, but the leap this time is much bigger.
The AI team that fits inside a prompt
Think of an AI agent as a cross-functional delivery team compressed into a single interface. It can wear multiple hats if you guide it properly. Architect. Developer. QA. Product analyst. Release engineer. All of them, depending on what you ask for.
AI doesn’t eliminate human roles; it elevates them to where insight, context, and decision-making live.
Someone with broad architectural understanding can now operate at a completely different level. You can design an architecture, get the AI to challenge it as if it were a peer, then break it down into user stories, UX flows, wireframes, sequence diagrams, build plans, and test plans. The AI can draft the lot, but only if you provide the context.
And this applies just as much to Developers, Product Owners, Product Managers, and Testers. Anyone who understands their domain well can suddenly work at a far broader level, with the AI picking up the mechanical work as long as you feed it the right context.
You still need the breadth and at times willing to go deep, but AI enables you to go broader You still need to understand what good looks like. You still need judgement. But you no longer need a room full of specialists to move something forward.
So will AI replace Product Owners, BAs, QAs, Architects, Developers?
This is the question everyone keeps dancing around. Will one person with an AI agent replace an entire cross-functional team?
In theory, yes. If that one person were perfect, had unlimited cognitive bandwidth, and could communicate with absolute precision every single time, then an AI agent could wear all the hats. Architecture, development, QA, product analysis, documentation, planning, all of it.
But that’s not reality. No one is perfect, and no one has that much headspace.
What agentic AI actually does is allow you to do more with fewer people. It compresses the execution layer, not the need for expertise.
You still want a QA expert making sure the test strategy is solid and the edge cases are covered.
You still want a Product Owner or Product Manager to shape business context, gather requirements properly, and communicate intent to the AI the same way they would to a team.
You still want an architect to ensure the solution aligns with organisational principles and fits sensibly within the wider ecosystem.
The difference is you don’t need eight full-time developers to grind through the backlog. You might only need one or two. And the PM, BA, Architect, and QA aren’t glued to a single initiative for months. They can work across multiple streams in parallel, move on to the next piece of work sooner, and keep overall flow moving faster than the old model ever allowed.
It’s not about replacement. It’s about amplification and leverage. The constraint shifts from “how many people can we assign” to “how clearly can we communicate and how well can we review”.
That’s where the transformation really happens.
A word of caution
There’s something that often gets glossed over in these discussions.
AI will supercharge teams that are already agile. And by agile I don’t mean companies that call themselves agile but run four-week sprints, release once a month, or somehow still operate on three- or six-month release cycles. That isn’t agile. That’s waterfall on a calendar.
I’m talking about the teams who truly embraced cross-functional working, DevOps, and continuous deployment long before AI arrived. The teams who ship daily. The ones who treat iteration as a muscle, not a ritual. Those teams will pick up agentic AI and immediately benefit because the mindset already fits. Small slices, rapid feedback, constant movement.
The organisations still weighed down by rigid processes, heavy handoffs, slow governance, and long release cycles will struggle. AI won’t save them. It will expose their weaknesses. To get the benefit, they will need to overhaul not just tooling but mindset, process, decision-making, and the way work flows end-to-end. Without that, they’ll fail to realise any of the upside.
Agentic AI rewards agility. It punishes inertia.
This is not a single-prompt fantasy
If you think you can drop one monster prompt on an AI and walk away with a perfect system, you’re dreaming. The AI only knows what you tell it. It doesn’t know the ten years of nuance in your head. It doesn’t know the constraints your business has been wrestling with. You have to walk it through the journey, step by step.
Working with an AI agent is closer to working with a development team using agile principles. Small increments. Constant review. Tight feedback loops. If a human team shouldn’t build a two-week story in isolation without a single show-and-tell, why would you let an AI do it?
A user story that would take humans five days can be implemented by an AI in minutes, but that speed is meaningless if you let it sprint off in the wrong direction. Keep your increments short. Thirty minutes of output. Review. Correct. Move forward again.
The AI is fast, but it is not magic.
The mindset you need
Agentic AI rewards architects who know how to think, structure, and communicate. It punishes architects who rely on vague hand-waving. It elevates people who know their domain deeply, because context becomes the fuel the AI depends on.
If you’re willing to engage in iterative dialogue, challenge the AI, refine the direction, and break work down into tight loops, you’ll get incredible results.
If you expect one perfect prompt to define your programme, you’ll fall flat.
Final thought
This isn’t the end of engineering teams. It’s the evolution of our roles. Agentic AI doesn’t remove the need for expertise, it amplifies it. It removes the drudgery. It removes the boilerplate. It accelerates discovery. It compresses feedback loops. It gives architects leverage.
But it only works if you accept that communication is your job, that you are not perfect, and that the AI needs the same clarity your human colleagues always did.
Use it as a partner, not a magic trick, and it will genuinely change how you build.