The article presents a systematic comparison of popular AI Agent Harness frameworks, including Claude Code, Cursor, and Codex, based on a team's internal project experience. It outlines the criteria used for evaluation, such as integration ease, scalability, and code quality impact, and explains why the team ultimately chose Trellis. The piece offers practical advice on setting up harnesses, managing prompts, and iterating with AI feedback. For overseas developers and tech leads, this is a timely resource as many teams are currently navigating the landscape of AI coding tools. The commercial value is significant, as it helps reduce trial-and-error in tool selection, potentially saving engineering time and improving productivity.
This article compares several AI Agent Harness frameworks (Claude Code, Cursor, Codex) and details a team's journey to adopting Trellis. It provides practical insights into the engineering considerations for integrating AI coding assistants into production workflows. This is valuable for teams looking to move beyond individual tool usage to systematic adoption.