Integrating AI agents into development requires clear requirements, architectural constraints, and automated quality gates. Relying solely on AI to generate features can lead to regressions and inconsistent code if the process lacks defined acceptance criteria and technical oversight.
In a demonstration project, a banking application was built using Flutter with a modular, feature-first architecture. The development process involved setting specific performance and design requirements, which allowed the AI to generate a functional, maintainable codebase without explicit architectural instructions.
Performance testing on a budget Android device (realme RMX3760) showed that with proper constraints, AI-generated code can meet strict benchmarks. For a test involving 12,000 operations, the system achieved a frame drop rate of 0.25% against a 1% threshold.
The total time spent on the demo project included approximately 27 hours of AI agent activity and 19 hours of actual elapsed time. This highlights that AI-assisted coding is not an instant process but requires significant time for requirement refinement, testing, and manual verification.
Teams must assign clear responsibility for task definitions and technical decisions. Automated quality gates are essential for merging code, and long-running AI cycles require strict time budgets and stop conditions to prevent infinite loops and resource waste.
