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Designing human approval gates for AI agent tool calls

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AI agent demos often stop once the agent decides to act, but real businesses need controls on what happens next. The author recommends classifying each tool an agent can call by risk, based on whether it affects external parties, moves money, or can be reversed. High-risk actions should be turned into proposals that a person reviews, including the agent's reasoning and the policy it relied on. Approval steps should live in the workflow itself rather than in prompts, and must handle rejection and non-response explicitly, with timeouts that never default to approval. The examples use n8n, but the approach applies to any agent framework.

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