Agentic coding: When should you let AI help build your application?

Agentic coding means using AI agents to take on software development tasks. A coding agent can explore an existing codebase, make changes across multiple files, run tests, track down bugs, and use what it finds to improve its own work.

The developer decides what needs to happen, gives the agent the context it needs, and reviews the result. With conventional AI coding assistance, you guide the work one step at a time. With agentic coding, you delegate an entire task.

How agentic coding differs from vibe coding

With vibe coding, you describe what you want and let AI turn it into a working application without having to understand how it was built. That makes it easy to put something together quickly, even with limited programming experience. It’s handy for a prototype, an experiment, or a simple internal tool.

Agentic coding brings coding agents into a professional software development process. Once you start delegating entire tasks, your role changes. In effect, you become the engineering lead of a development team. You assign work, set the technical direction, and make sure the pieces the agents deliver fit together into a coherent system.

That takes the breadth of experience you’d expect from a senior developer. You need to judge whether a solution fits the architecture, whether existing integrations will keep working, and whether the software is secure and maintainable. Even if the agent reports that every test passes, you need to spot what those tests missed.

You draw on that experience before the agent starts. Which architecture and design patterns should it follow? What security requirements must the software meet? What does ‘done’ look like? Clear engineering principles and acceptance criteria give the agent enough direction to work independently and give you a way to judge the result.

The agent also needs a working environment where it can access the relevant code and architectural decisions, reproduce the build, and run tests. That setup is often called a harness. It also defines the agent’s boundaries. Which files can it access? Which commands can it run? When should it stop or ask for help? Keep production credentials out of its reach.

When does agentic coding make sense?

The complexity of an application tells you surprisingly little about whether to use low-code or build custom software with coding agents. A complicated business process can fit comfortably within a low-code platform. A small application can still call for custom development.

Start with three questions.

1. What does the application need to do?
2. What requirements does your existing IT environment impose?
3. Do you have the expertise and capacity to build and run the application, or will you bring in outside help?

If a platform gives you the integrations, access controls, and tools you need to deploy and run the application, there’s little reason to build those capabilities yourself. Custom development becomes more attractive when important requirements are hard to meet within the platform. You might need a specific integration, tighter control over performance or deployment, or more control over how sensitive data is handled under the hood.

Your existing IT environment will also shape the decision. Can the application integrate with your central identity provider? Does it need to run on a private network or in your own cloud environment? Existing API contracts and deployment pipelines can narrow your options further. Check which source code and data you’re allowed to share with the AI provider.

Think beyond the first release. Do you have people who can make technical decisions, review the agents’ work, and handle updates, security, and incidents once the application is live? You can build that capability in-house or work with an external partner. The expertise you can count on over time determines how much custom software you can realistically support and how much you want a platform to handle.

Often, the answer is somewhere in between. A low-code application can call a custom API for the one part you need full control over.

Coding agents can make that custom work faster and more practical by taking on a good share of the implementation. Start with well-defined tasks where you can verify the result. If the codebase is poorly documented and lacks reliable tests, invest in that foundation first.

A fast agent still needs a good engineer

A coding agent can write a lot of code in a short time. It can also write a lot of bad code in a short time.

That changes where developers spend their time. They write fewer lines themselves and spend more time designing the solution, giving agents the context they need, and coordinating the work. That calls for the skills of a lead developer who can scope tasks, spot architectural mistakes, and judge how changes fit together.

Before the agent starts, pin down exactly what needs to change. Which existing patterns should it follow? What must it avoid? How will you verify that the change works?

Say you’re adding an endpoint to an existing customer portal. You define the API contract and access rules. One agent implements the change. Another can review the tests and check edge cases, such as whether a customer could access another customer’s data. You keep track of dependencies and review the change as a whole. Run automated tests and security checks in CI, and require a human review before merging. A second agent’s approval is no substitute for those checks.

When you build custom software, you also own the entire software development lifecycle. Agents can help with implementation, testing, and documentation. Your team still handles deployment, monitoring, dependency updates, and incident response. A platform may cover some of that groundwork. With custom software, you arrange it yourself or use managed services. Writing code faster does little to reduce the work that comes after launch.

Start with what you need

Agentic coding can make custom development viable for more applications. Work that once took too much development time may now be worth tackling, provided review and rework don’t eat up the time you save.

Sometimes the right solution already exists. Sometimes low-code fits the bill. Sometimes you need custom software. And often a mix makes the most sense.

Look at the full cost. A platform licence is a visible line item, and the fee may cover much of what you’d otherwise have to run yourself. The costs of hosting and maintaining custom software are easier to underestimate and can end up higher than the platform bill.

Set a budget and agree when to stop for each task. Track AI usage, review time, and rework per accepted change. Then compare the total cost of custom development, including hosting and maintenance, with the platform option. A cheap first release can turn into an expensive application to run.

Start by asking what the business needs, what you can reuse, which requirements really matter, and who will run the application once it’s live. Then decide what to build and how much of the work to hand over to AI.

The best approach for you?

Agentic coding, low-code, or vibe coding? Work through our decision tree to discover which approach best fits your app.