Why AI Agents Don’t Automatically Make Your Engineering Team Faster

AI agents like Codex somehow aren’t making your team faster? Maybe this is why:

Claude Code and Codex can generate features for you in minutes. They can autonomously find and fix nasty bugs. But they bring nothing if the foundation is brittle. Here are 10 software development principles that still apply in 2026:

1: Main focus on a single task:

Context switching is what impairs your performance the most. Both for individuals and in the team.

2: Keep files small:

Long files are something neither the engineer nor AI wants to read. With long files, AI makes worse decisions when it inserts new information or features. Read operations fail, and the context gets poisoned.

3: Divide and conquer wins:

When a task is too large, the probability of errors rises sharply. Large tasks are just small, digestible tasks in a trench coat that are much, much easier to solve in isolation.

4: Code should document itself:

“doSomething” can mean “deleteUser” or “confirmPayment”. So name it that way too. Claude will thank you too, because it has less thinking to do.

5: Review is the bottleneck:

When your company releases a new version, AI does not take responsibility; you do. Automated tests and AI-driven code reviews make generated code easier to handle, but in the end, you have to stand behind it. An effective review process is mandatory when an agent creates 10 pull requests a day.

6: When requirements are unclear, the PM does not get what they want:

Vague wording in requirements can send entire sprints in the wrong direction. Vague wording in prompts wastes time and tokens in agentic coding. Clear communication, resolving misunderstandings, and closing gaps remain indispensable.

7: Architecture and software design make up a large part of the work:

Especially early design decisions that do not fit what the product is supposed to become hurt financially and in terms of time. Anyone who takes shortcuts in this part, and in every further iteration, does not get faster, but slower.

8: If you do not know what you are doing, you still do not know with AI by your side:

Good engineers get stronger with AI. Weak engineers do more damage with AI; unless they use AI to learn. People are shocked when they hear this, but guaranteed even more shocked when they see the scale of the destruction.

9: Documentation ages faster than you think:

The closer the documentation is to the code, the greater the chance that AI updates it when the behavior changes. The farther away it is, the faster it becomes outdated. Large documentation texts fill your agents’ context window before they can build anything at all. The more efficient the documentation, the lower the risk of context rot.

10: The best piece of code is the one you do not need:

Just because code has become cheaper does not mean you should generate more of it: every line of code creates maintenance effort sooner or later. “Nice-to-have” often also means “even-nicer-to-not-have”, even if the new user flow is generated in five minutes.

A lot is changing right now, but not everything. If you are currently introducing agentic coding in your company and running into problems, those may only be symptoms of deeper, longer-existing causes. And those can be found and solved.

I am currently giving away my AI Delivery Audit! If you do not know whether AI is making your product development faster, this is for you.