Automated Testing

The Seduction of the Instant Plan Modern AI agents like Claude Code create a psychological pressure to move fast. When you feed a complex feature request into a tool powered by Claude 3 Opus, it returns a structured plan almost instantly. This speed creates a false sense of security. I’ve noticed a recurring mistake: I treat the plan as a mere formality rather than a blueprint. Skipping the fine details—like how a many-to-many relationship handles cascading deletes or the specific length of a slug—results in immediate technical debt. If you don't catch these implementation details during the plan phase, the AI proceeds with assumptions that might not align with your specific project constraints. Your role as a developer is shifting from "writer" to "architectural reviewer," and that shift requires a level of focus we often bypass in our rush to see the code. The Illusion of Completion The second pitfall occurs after the code exists. When the visual interface looks right and the automated tests pass, it is tempting to mark the task as done. However, passing tests do not guarantee clean architecture. I recently found that Claude Code used an outdated Livewire pattern for computed properties. While the code functioned, it ignored modern PHP attributes now standard in the framework. This "vibe coding" approach—where we trust the output because it works on the surface—slowly erodes project maintainability. If the AI uses three different patterns to solve the same problem across your codebase, you lose the cohesion that makes a project future-proof. Practical Guardrails for AI Workflows To fight the urge to be lazy, you must enforce a strict review protocol. First, never hit "proceed" on a plan until you have verified every database constraint and UI component choice. Second, read the AI's summary of modified files as carefully as you read the code itself. This summary often reveals the architectural decisions—like helper placements or property patterns—that you might miss while scanning a long diff. Maintaining Ownership in an Automated World Ultimately, the responsibility for the codebase remains yours, not the LLM’s. An AI agent cares only about fulfilling the current prompt; it doesn't care if your project is maintainable two years from now. Stay disciplined. Reviewing the small details today prevents the massive refactoring sessions of tomorrow. We must remain in control of the "why," even as we automate the "how."