When you work with an AI coding assistant, there's a rhythm that emerges. You describe intent. The agent proposes. You evaluate. You adjust. You describe again. This is the inner loop.
Most tools focus on the proposal step — making the AI's output better. But the quality of the loop depends on every step. How clearly can you express intent? How quickly can you evaluate a proposal? How efficiently can you course-correct?
The best agentic development experiences minimize friction across the entire loop, not just the generation step. This means better context management, clearer feedback mechanisms, and interfaces that support rapid iteration.
Tools that understand the loop outperform tools that just generate better code.