The generated code by AI is faster, but manual criticism remains the limitation. AI has had a significant impact on the productivity of coders. Companies have started to use AI agents for automated parts of the coding process, reducing human responsibility.

This has led to a remarkable increase in the number of codes created, but it has opened up a new challenge: manual verification. Coders are forced to approve every single line of code before it can be included in a project. This manual verification process is very time-consuming and requires a lot of expertise.

Moreover, coders are forced to respect a certain level of precision, which can be difficult to achieve with the help of AI. According to a recent study, the effectiveness of these AI agents is limited. In fact, the code is not always syntactically or semantically accurate.

The accuracy of the code depends very much on the programming style of the coder, which can be influenced by trends in the field of AI. Another important aspect is the complexity of the manual verification process. Coders need to check every single line of code, which can be very tiring and demanding.

Coders may also be forced to modify the code to make it conform to security norms or specific project context requirements. However, even though the accuracy of code is limited, AI continues to represent a significant advantage in terms of productivity for coders. Companies that use AI agents have started to achieve results more rapid and efficient compared to their competitors who do not use these technologies.

The importance of manual verification becomes apparent when considering the consequences on software produced. The code created by AI has never been adequately tested and may contain errors that could lead to system failure in the future. This is mainly due to the lack of a manual verification phase that ensures the quality of the code.

Finally, AI has opened new opportunities in the software field, but also new problems. Companies that use AI agents are forced to find an equilibrium between production speed and software quality. It is necessary to approach this issue with an integrated approach that combines the efficiency of AI with human expertise to ensure high-quality software.

END SOURCES