Agentic AI in Software Engineering: Evidence from Production Software and Future Directions for Developing Countries

The significant research contribution is the empirical evaluation of agentic AI coding tools in a production-style software environment. The study uses 160 tasks performed by 16 professional .NET developers on the nopCommerce platform to quantify success, task-complexity effects, human intervention, token exhaustion, and failure patterns. It demonstrates that task complexity significantly affects agentic AI performance and provides practical recommendations for human-in-the-loop adoption of agentic AI in Bangladesh and similar developing-country software environments