AI Code Generation Raises New Quality Risks

Artificial intelligence is generating code at an unprecedented rate, outpacing the ability of engineering teams to manage it. While this speed brings productivity gains, it also introduces significant quality risks that many leaders have yet to fully acknowledge. Technical debt accumulates more rapidly, code repeats itself, and security vulnerabilities emerge in unforeseen places due to a lack of oversight.

The speed of AI-assisted development appears to be a win only if the review process can keep pace. Mike Vizard spoke with Mladen Lazic, CTO of Scopic, to explore how to prevent AI-assisted development from becoming a cycle of debt. Lazic emphasizes that reviewing AI-generated code is more challenging when engineers were not part of its creation.

Context is absent, intent is unclear, and reviewers cannot rely on their usual mental shortcuts. His solution is to keep developers involved from the beginning and treat AI tools as junior developers in need of guidance, rather than autonomous feature creators. Lazic cautions against the common mistake of an AI agent writing code, generating tests, and reviewing the output, which effectively grades its own work.

Scopic is adopting a different approach, employing separate agents for coding, pull request review, and test creation. Some testing agents are designed to be blind to the source code, validating against requirements instead of the generated code. This separation transforms AI QA from a rubber stamp into a genuine check. Looking to the future, Lazic predicts that engineers' roles will transition from production to instruction, monitoring, verification, and improvement.

This shift redefines AI code quality as a long-term concern for DevOps, security, and application leaders, rather than a temporary issue. Teams that establish robust gates, enhance observability, and implement independent QA will be able to produce higher-quality software more efficiently, avoiding the debt that others accumulate silently.