NHN Cloud Developer's Experience Building an AI Harness: There Is No Silver Bullet

Lee Jae-hyung, a developer on the Messaging Platform Development Team at NHN Cloud, shared his experience of building an AI harness over the course of a month to use AI agents like team members. Previously, AI sessions were reset every time, requiring the team context to be explained repeatedly. However, by meticulously refining the harness, he reached a level where the AI could independently understand and execute work contexts.

The Importance of Structure and Records

The core of building a harness is not storing information, but storing how to find information. Instead of a single file exceeding 500 lines, the author designed a directory structure separating service-specific knowledge from team knowledge. Additionally, through three types of records—work-log, decisions, and incidents—the AI was enabled to learn from past mistakes and the reasons behind decisions. To prevent the AI from automatically incorporating incorrect knowledge, a rule was established where AI suggestions are accumulated in harness-backlog, and humans decide whether to finally reflect them.

Three-Layer Safety Measures and the Absence of a 'Silver Bullet'

To prevent indiscriminate execution by the AI, a three-layer approach was applied: prompt text rules, structural enforcement, and permission blocking. Specifically, physical restrictions were imposed by granting accounts without write permissions or allowing only verification scripts. The author emphasizes that although completed harness templates or automated construction tools exist, a harness lacking the 'why'—which captures each team's unique context and trial-and-error—is useless. Ultimately, while the model determines the performance of AI tools, the depth of utilization is determined by the context that users refine little by little every day, concluding that there is no silver bullet that can be completed overnight.