· ai-devtools
Fast Code, Slow Feedback: Fixing CI for the Age of AI Agents
AI coding agents write code in minutes, but every agent-generated pull request still waits on cold checkouts, fresh installs, and full test suites.
Agents also multiply pushes, retries, and reruns, so every pipeline inefficiency, and the compute bill, gets amplified. This article explains how faster checkouts, caching, test selection, and flaky-test handling can speed up a slow monorepo, and where each technique can break. The goal is to cut feedback time for developers and agents, and to reduce CI spend, without trading away reliability.
Agents Changed the Shape of CI Load Writing code got fast. GitHub reports that by September 2026 more than 40% of pull requests on its platform began with an agent's first draft, up from 17% in March, and that Actions jobs grew about 4.8x in a year. Those counts cover only agents GitHub can detect, but the direction is clear.
Cost follows a simple formula: runs × minutes per run × price per minute. Agents raise the first term with more pull requests, more pushes per pull request, and round-the-clock activity.
