Build AI Agent MVPs Around Human Verification, Not Maximum Autonomy

Most AI agent prototypes prioritize autonomy over reliability, but developers are advised to instead focus on the smallest workflow a human can verify. The recommended approach divides tasks into three categories: deterministic code steps, model-based judgment, and actions requiring human approval before execution. Defining strict typed contracts for each tool — including inputs, outputs, permissions, and failure responses — makes agents testable and prevents unintended permission creep. Early releases should favor read-only tools, with humans approving any high-impact external actions such as sending messages or modifying production data. The goal of an MVP is not to simulate a fully autonomous system, but to demonstrate that a narrow workflow can produce repeatable, verifiable value.