When working with AI agents, one lesson has become increasingly clear to me: don’t start by telling the agent exactly how to do the job. Start by asking what it sees. Most of the time, we already know the outcome we want, but when we give AI overly detailed instructions too early, we also force it to follow our existing way of thinking. The AI may still deliver the same result, but the process can become unnecessarily rigid, complex, or inefficient.
I now prefer to begin with a simple request: “Brief me on what you see, what you think is not optimal, and what you recommend.” Only after that do I decide what should be executed. This changes the role of the AI agent from an instruction follower into a problem-solving partner. It may identify unnecessary steps, duplicated processes, better automation opportunities, more efficient architecture, overlooked risks, or a simpler way to achieve the same outcome.
The workflow I increasingly use is simple: observe, diagnose, recommend, challenge, and execute. First, let the AI understand the environment. Then let it identify inefficiencies, suggest improvements, challenge the current approach, and only after that proceed with execution.
One of the biggest mistakes in using AI agents is assuming that better prompting always means giving more instructions. Sometimes, better prompting means giving the agent enough freedom to discover a better path. The objective should remain clear, but the method does not always need to come from us.
That is where AI agents become genuinely useful-not just automating work, but helping redesign how the work should be done.
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