Engineering a security harness for AI coding agents

AI coding agents can assist with consequential security tasks, but their effectiveness and safety depend on a security harness that provides trusted context, limits permissions, governs workflows, and verifies results rather than relying on prompts alone. The proposed framework centers on seven questions: defining the agent’s specific job, supplying the smallest sufficient set of authoritative evidence, separating authority levels from reading through enforcement, requiring validation and approvals before consequential actions, evaluating process quality alongside results and real-world outcomes, containing failures and adversarial inputs through fail-closed controls and limited blast radius, and safely managing updates through versioning, testing, monitoring, rollback plans, and clear ownership. Controlled workflows should resemble state machines, with explicit approval gates, bounded retries, stop conditions, and separate permissions for preparing, publishing, and enforcing changes. Deterministic evidence systems can also reduce tool calls and token use by preventing models from repeatedly reconstructing known security facts, while retaining human reviewers’ ability to inspect, challenge, and reject agent recommendations.

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