Agentic development has the potential to change how software teams plan, build, test, secure, and maintain code. For enterprise leaders, the challenge is not whether AI can help. The challenge is knowing which use cases are mature enough to evaluate, which workflows are worth prioritizing, and how to align adoption with engineering governance, security, and scale.
This guide gives engineering leaders a practical framework for evaluating agentic AI use cases across the software development lifecycle. It is designed to help teams focus on real workflow impact instead of generic AI promises.
