AI agents write PostgreSQL like Python (vvka-141.github.io)

AI agents are increasingly being tasked with generating SQL code, but a recent field report highlights significant challenges when these agents convert PostgreSQL procedural code into Python-like syntax. The report reveals that while AI-generated SQL demonstrates a strong capacity for producing correct output, it often employs idioms that are more suitable for Python, leading to inefficiencies and potential pitfalls in database operations. For instance, agents frequently use exceptions to handle ordinary outcomes, which can inadvertently create costly subtransactions in PostgreSQL, impacting performance under load. The significance of this finding lies in the implications for database architecture and SQL best practices. The misuse of exception handling not only complicates code readability but also risks efficient database operation, particularly as applications scale. The report calls for a shift towards clearer, more efficient coding patterns, such as returning NULL for "no action" situations instead of raising exceptions. By leveraging built-in PostgreSQL functions like `pg_input_is_valid()`, developers can validate input without incurring a transaction penalty, thus enhancing application performance and reliability. This highlights the necessity for AI agents to be better informed about the underlying database mechanisms and to adopt coding practices that align with database management best practices.