Harness engineering: how to make AI coding agents reliable and secure

Harness engineering is presented as the design of the systems surrounding an AI model—such as context, tools, constraints, execution environments, and feedback loops—to make coding agents more reliable in production. It extends beyond prompt and context engineering by governing how an agent acts, validates work, and corrects errors through “guides” such as rules files and system prompts, and “sensors” such as linters, compilers, tests, and quality gates. The text argues that because AI-generated code can be inconsistent and may introduce vulnerabilities or risky dependencies, security checks should be embedded in the agent workflow rather than deferred to later review stages. It recommends starting with an existing coding-agent platform, documenting project conventions, adding automated correctness and security feedback, converting recurring failures into permanent safeguards, and measuring effects on rework and pull-request delays. It also promotes Endor Labs’ AURI as a security integration layer for agents including Claude Code, Codex, and Cursor.

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