Why AI Agents Must Have an Execution Boundary Between Intent and Action

AI agents capable of modifying external systems pose a reliability and safety risk when their decisions directly trigger real-world side effects without structured controls. A core problem arises in scenarios like publishing a page, where a timed-out response can cause duplicate actions or unintended consequences if the agent retries without checks. The proposed solution is an execution boundary — a dedicated application layer that handles validation, authorization, policy enforcement, idempotency, approvals, and auditing separately from the AI model's reasoning. Rather than acting directly, the agent proposes a structured action object, and deterministic application code decides whether that action is permitted and safe to execute. This separation ensures the model contributes intent while the application retains full authority over consequential operations.