In this episode of The Agile Software Engineering Deep Dive, Alessandro Guida explores the difference between generating code and learning software engineering.

Modern coding AI has been trained on enormous quantities of publicly available software. Some of that code is excellent, but much of it is also outdated, inefficient, insecure, poorly tested, difficult to maintain, or disconnected from the realities of commercial software development.

The episode examines several recurring concerns in AI-generated software: black-box code that developers may accept without fully understanding, inefficient implementations, error handling that does not integrate with the surrounding product, inconsistent security, and user interfaces created without user research, workflow analysis, prototyping, or human feedback.

It also considers a more systemic risk: what happens when AI-generated code increasingly becomes part of the training material for future AI models? Weak patterns may be copied, normalised, and amplified until commonly generated code is mistaken for well-engineered code.

More agents do not automatically mean better software. Ten coding agents today and fifty tomorrow may simply produce more code, more complexity, and more work that still requires human review.

AI can be an extraordinarily valuable engineering tool, but it does not assume responsibility for the result. The engineer remains accountable for understanding, testing, securing, maintaining, and operating whatever ultimately enters the product.

The episode concludes with The Old Grumpy Engineer’s Trust Test, a humorous examination of whether engineering trust should ever be granted automatically-and a brief, nostalgic appearance from Clippy.

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