The Verification Bottleneck in AI-Generated Software

As AI coding agents become increasingly capable of generating software rapidly, the primary challenge in development has shifted from writing code to verifying it. The author argues that while AI can produce functional code in minutes, it often lacks the nuance to handle edge cases, such as ensuring a password reset link is single-use. Because AI-generated code is often disposable and subject to frequent changes, the focus must shift toward maintaining durable, executable specifications. By using behavioral testing as a contract, developers can separate the generation of code from its verification. The author proposes a feedback loop where deterministic testing tools provide evidence of failure back to the AI, allowing it to iterate until the software meets the specified behavioral requirements. Ultimately, the goal is not just to generate code faster, but to ensure that the delivered software is correct and reliable.