Agents Won't Replace Software Engineers

### Intro

AI is changing software development quickly.

Modern programming agents can now easily read and write across any codebase. Tools like Claude Code, Codex and GitHub Copilot are no longer just autocomplete tools as they can now work on large chunks of dev work, for the most part independently.

It sounds dramatic and has led to the claim: >"Software engineers are going to be replaced"

This claim misundererstands what good software engineers actually do. It has nevver been just typing code, it is translation.

All businesses have messy requirments, exceptions, processes, legacy tools, spreadsheets, approvals, operational risks and edge cases. A SWE's (Software Engineer) job is to understand all off that and turn it into a system that is useful, secure and maintainable.

AI speeds up that translation. It does not remove the need for someone to decide what will be translated in the first place.

### Code was never the whole job

A lot of people think software dev looks like: > Someone has an idea -> a dev writes code -> software appears

In reality it looks more like this: > Business has a problem -> people describe it horribly -> the real workflow is uncovered -> edge cases are found -> figure out the data structure -> the system is designed -> the software is built -> system is tested -> code is reviewed for maintainability, security and logic purposes -> software is deployed

The code is only one part of that process. For internal business systems, this matters even more.

An intranet has to fit the way the business actually works. It needs to handle permissions, roles, audit trails, messy data, human behaviour and future changes.

That isn't a coding problem, this is a business architecture problem.

### What agentic dev actually changes

Agentic development changes thge speed of delivery. Instead of manually writing every small piece of code, engineers can now outsource programming tasks to AI agents.

This is a big deal.

It means good engineers can move faster. Prototypes can be created more quickly, bugs can be investigated with more context and teams can spend more time thinking about the system, rather than fighting boilerplate.

Although speed isn't the same as quality, AI can create code quickly but someone still has to ask:

• Is this the right feature?
• Does this match the business process?
• Will it scale as the company grows?
• Is the data model correct?
• Is this secure?
• Can another developer maintain it?
• What happens when the workflow changes?

The winners will not be the people who blindly accept AI-generated code.

### Why vibe coding is useless for serious business software

"Vibe coding" has become a popular phrase for making software by prompting AI and just accepting what it creates and steering by feel rather than by any proper engineering principles.

Vibe coding is not useless for everything, it can ber useful for experiments, prototypes, personal tools and quick demos but for serious business software vibe coding is useless. This is because business software has consequences, if a personal prototype breaks, it's annoying but if an internal operations sdystem breaks, people lose time, customers get poor service and the business loses trust in your tool.

A vibecoded system may look impressive on day one, but the real tests come later:

• Can It handle real users?
• Can it cope with unusual cases?
• Can it be extended?
• Can it be secure?
• Can it be maintained six months from now?

Most vibecoded systems fail because they skip the thinking that makes software reliable.