Pragmatic Loop Engineering for AI Coding Agents

Kent shows how he closes agentic loops in real Cursor workflows: manual testing, PR review loops, Cursor Automations, and the stop conditions that keep humans in the right place.

• (00:00) - Loop engineering before the name
• (00:11) - Boris on loops
• (00:33) - Better with Kent
• (01:01) - What the loop is
• (02:59) - Tests were already a loop
• (03:24) - Browser mode and manual testing
• (05:35) - Human-driven PR looping
• (06:43) - My first real loop
• (08:37) - Boundaries and stop conditions
• (09:37) - Cursor Automations
• (11:29) - Trading compute for attention
• (12:24) - Homework
• (12:52) - Closing

Better with Kent — durable skills for people who ship software.

Loop engineering sounds new, but Kent has been building toward it through a series of practical workflow improvements: automated test loops, Cursor browser mode, Cursor Cloud Agent manual testing, pull request feedback loops, and finally Cursor Automations.

This episode walks through what a useful agentic loop actually needs: a trigger, an act/observe cycle, a stop condition, and a human boundary. Kent shows how he uses agents to mark PRs ready for review, respond to AI reviewer feedback and CI failures, and ping him when the loop has reached its stop condition.

The key idea is not removing the human. It is widening the loop so more verification happens before your attention is required.

Links

• Better with Kent
• How I Build Web Apps in 2026
• Manual testing example PR
• Manual PR feedback loop example
• First informal self-running loop PR
• First formal PR loop example