Harness Paradigm Simplifies Enterprise AI Agent Deployment and Governance

While frontier models have drastically reduced the cost of writing custom AI code, the expenses associated with reviewing and maintaining these diverse, bespoke solutions remain high for large enterprises. Current approaches often involve off-the-shelf products or custom graph-orchestration frameworks, which are limited in scope and still require significant custom development and review. This research advocates for a "harness paradigm" as a third option, treating the coding-agent harness as enterprise infrastructure. Previous findings suggest that harnesses are effective for task-level work, often outperforming more complex architectures, and that harness choice significantly impacts agent performance more than model choice. The primary barrier to enterprise adoption has been governance. The proposed architecture aims to close this governance gap by using one unmodified harness as a central backbone. This means the core code remains identical across deployments, simplifying review processes to just an instructions file. Key mechanisms include credential-scoped tooling, where backends use generic request tools with scoped credentials, and externalized authorization logic, allowing a single artifact to serve multiple functions (cron, chat, terminal). Code registration becomes a side effect of pushing code, streamlining audits.