Why code reaches main slower with AI agents

Using AI agents to write code significantly accelerates development, but it creates an unexpected paradox: changes are taking longer to pass through review and QA stages. According to data from Faros AI, CircleCI, and DORA, the increased volume and frequency of Pull Requests are overwhelming review processes, creating bottlenecks in the Software Development Life Cycle (SDLC). The article analyzes why the gains in coding speed are being negated by integration delays. The authors emphasize that automated code generation requires a rethink of quality management and delivery processes. To address this, they suggest focusing on optimizing DORA metrics and identifying stages where changes most frequently stall. The article helps developers and team leads understand how to balance the use of AI tools with CI/CD efficiency to accelerate the path of code to production rather than slowing it down.