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Harness engineering positions the AI model within a broader control architecture rather than treating it as a standalone decision-maker. In logistics workflows, this architecture establishes authoritative data sources, limits decision rights, orchestrates multistep processes, maintains durable state, and applies deterministic validation before actions are executed.
The approach is particularly relevant where decisions can affect service, cost, contracts, or customer commitments. By isolating failures, controlling retries, and retaining a run receipt of inputs, actions, validations, and outcomes, organizations can make AI-supported logistics processes more auditable and dependable at scale.