Your AI Agent MVP Does Not Need More Autonomy

Most AI agent MVPs start with the wrong question: How much can we make autonomous? A better first question is: What is the smallest useful outcome a human can verify? The difference matters. An autonomous demo can look impressive while hiding unreliable decisions, unclear permissions, and failure states that nobody has tested. A narrow, reviewable workflow is less dramatic, but it can become a real product. 1. Draw the boundary before choosing tools Split the workflow into three kinds of work: Deterministic steps: validation, parsing, database reads, calculations, and format conversion. Model judgment: classification, summarization, ranking, and drafting where uncertainty is expected. Human approval: sending messages, changing production data, spending money, or publishing externally. This boundary tells you where an LLM is useful and where ordinary code is safer. It also prevents the agent from quietly gaining permissions just because a demo needs to look se