Harness Engineering Guide for Designing Coding Agent Environments

The performance difference of coding agents is determined by the environment (Harness) surrounding the agent, not by the model or prompt. Harness Engineering refers to the practice of optimizing the context, tools, and surrounding environment available to the agent while keeping the model and agent fixed as black boxes, thereby meeting the organization's non-functional requirements (such as security, reliability, and maintainability).

The core lies in effectively conveying the organization's private process data (the submerged part of the iceberg), which is not contained in the model's weights, to the agent. To this end, the following 12 core propositions are presented:

• Worker Fixation and Environment Verification: Fixing the model/agent and optimizing the environment
• Utilization of Private Process Data: Leveraging the organization's unique procedures and data
• Full Ownership of Work: Managing a single flow from decomposition to proof by the agent
• Context Routing: Efficiently managing knowledge repositories and active task sets
• Tool Utilization Capability: Completing the loop from tool discovery to recovery
• Learning Through Repositories: Using code as prompt material
• Autonomy and Permissions: Maximizing autonomy within specified permissions
• Result Proof and Feedback: Verifying results in the actual environment and converting feedback into infrastructure

This methodology goes beyond simple prompt modification, aiming to build a retrieval-optimized context bundle that enables agents to understand and execute the organization's local ontology, quality standards, and exception handling history.