Agentic development · Guide
What is agentic development? A business owner’s guide
AI is changing software development quickly, but not always in the way people think. For a lot of business owners, AI still means asking ChatGPT or Claude a question instead of searching Google. Software development has moved well beyond that.
AI agents can now write code, build interfaces, create tests, investigate problems and scaffold entire applications. Used properly, they can dramatically reduce the time and cost involved in building digital products. There is an important distinction between asking AI to build something and using AI as part of an experienced engineering process. That distinction is what we mean by agentic development.
What is agentic development?
At Ainley Digital, agentic development means combining an experienced senior software engineer with AI development agents to build websites, platforms and bespoke software. The engineer remains responsible for the project. The AI does not replace the thinking, decision-making or engineering experience. It helps execute those decisions much faster.
Day-to-day, that means less overhead, fewer unnecessary meetings and more time spent actually building the product. A traditional software project might involve an account manager, project manager, technical lead, multiple developers and QA resource. With an agentic-first approach, a senior engineer can work directly with the client and orchestrate AI agents to deliver much of the implementation.
That creates a much thinner delivery team, and a thinner team means lower overheads, faster delivery and potentially lower project costs. It also gives us the ability to scale quickly. If a project suddenly needs significantly more development capacity, AI agents can be spun up around the existing engineering direction without weeks of recruitment or onboarding.
Agentic development is not vibe coding
The phrase "vibe coding" has become incredibly prominent over the last couple of years. To me, vibe coding means giving an AI a rough idea of what you want, accepting what comes back and assuming that because it appears to work, it must be good software.
That is not how we work.
In fact, agentic development is almost the opposite. Before development starts, the brief is reviewed and challenged by a human. We investigate the edge cases. We think about what might go wrong. We consider security, performance, maintainability and how future features could affect the decisions we make today.
When an agent writes code, we review that code in much the same way we would review work produced by another developer. If it needs improving, it gets improved. If something should be engineered by a human, it is. If we believe a particular part of a system should never be left to an agent, it isn't.
The important thing is that AI provides acceleration, not accountability. The senior engineer still owns the result.
AI gives us a starting block, not a finished product
One of the biggest misconceptions around AI development is that you simply give the AI a specification and wait for the finished software to appear. You can certainly do that. The problem is knowing whether what comes back is actually any good.
An AI agent might produce something that appears correct while missing an important security consideration, creating technical debt or making a decision that becomes a serious problem six months later. Experience changes the way you work with those agents. If we're asking an agent to build an authentication system, for example, we're not simply saying:
"Build user authentication."
We know the common pitfalls. We know the security considerations. We know the awkward edge cases that tend to appear in production systems. That experience influences the prompts we give the agent, the architecture we choose and the code we accept. The first output becomes the starting point. Then we interrogate it, improve it and turn it into production-quality software.
That is how you avoid what has become known as AI slop: software that looks impressive on the surface but hasn't had enough thought put into what sits underneath it.
Where 20 years of engineering experience matters
I've spent around 20 years building software across agencies, internal teams and contract roles. One of the biggest lessons from that experience is that good software doesn't necessarily require a big team.
It requires the right decisions. Agentic development allows us to take that experience and operate with a much thinner delivery model. We don't need layers of account management between the client and the person making technical decisions. We don't need junior developers to complete large amounts of implementation work. We don't need project management overhead simply to coordinate a large delivery team.
The person you talk to is the person responsible for building the product. That removes a lot of the cost associated with the traditional agency model while keeping senior engineering oversight at the centre of the project.
A real example: from an estimated two months to nine days
We recently delivered a private community platform designed to support people struggling with their mental health or simply finding life difficult. The platform allows the owner to publish content, enable likes and comments, manage the community and moderate everything through a single administration area.
Using a traditional agency delivery model, a project like this could reasonably involve:
a couple of weeks of discovery
another week of planning
four or five weeks of development
testing and release
Using our experience alongside an agentic development workflow, we delivered the project in nine days. That doesn't mean we skipped the thinking. It means we compressed the implementation. The engineering decisions were still made. The features were still tested. The product was still reviewed.
We simply removed a significant amount of the time traditionally spent translating decisions through a larger delivery team and manually producing every line of implementation code. The client was delighted with the finished platform.
For us, that is where agentic development becomes interesting. Not because AI wrote some code, but becuase the client received the product they wanted dramatically faster.
What AI doesn't replace
There are parts of software development we don't believe should simply be handed over to AI. Testing is one of them. We use automated testing heavily, but we believe engineers should remain directly involved in validating what they build, and that means being at the keyboard writing those automated tests.
Our projects use automated tests to check functionality as features are added. We also write end-to-end tests that replicate real user journeys and run repeatedly as the project evolves. Automated testing is only part of the process.
We also manually use the software. We click the buttons. We submit the forms. We try the awkward combinations. We use features in the way a real customer would.
Ultimately, software isn't successful because a test suite says it works. It is successful because real people can use it reliably.
What happens without experienced oversight?
This is where the difference between agentic development and vibe coding becomes important. Without experienced engineering oversight, it is easy to build something that works today while creating problems for tomorrow. What happens when the next feature changes part of the existing architecture? What happens when usage suddenly increases? What happens when a bug appears in a part of the system you don't understand? What happens when your application starts handling sensitive customer information?
AI can help solve all of those problems. You still need someone capable of recognising that the problems exist in the first place. That's the role of the senior engineer.
What kind of projects work with agentic development?
Almost any digital project can benefit from this model. At one end of the scale, that could be a marketing or brochure website. At the other, it could be a bespoke business platform, analytics system or full-service application integrating with multiple external platforms and APIs.
We use the same principle across all of them: senior engineering judgement directs the work, AI agents accelerate the implementation. The complexity of the project changes. The principle doesn't.
Why not just hire a developer who uses AI?
That's a reasonable question. For some businesses, hiring an internal developer absolutely makes sense. The difference is flexibility. A full-time developer comes with the cost and commitment of a permanent employee. They also have a finite amount of development capacity.
With Ainley Digital, you're accessing senior engineering experience without necessarily taking on the cost of another full-time employee. When a project needs more capacity, we can scale the delivery team quickly by bringing additional agentic development resource around the same senior engineering direction.
Instead of recruiting another developer and spending weeks bringing them into the project, we can increase implementation capacity almost immediately.
The future isn't AI instead of developers
There is a lot of discussion about whether AI will replace software developers. I think that's the wrong question. The real change is that experienced engineers can now produce significantly more than they could before. AI can generate code extremely quickly.
Understanding what code should be written, how the system should work and whether the result is actually good enough to release still requires engineering judgement.
That is where agentic development becomes powerful. It doesn't remove the senior engineer. It gives them leverage.
For clients, that means something very simple: senior-quality software, delivered with far less of the overhead associated with a traditional development agency.