Agentic Product Design

An agentic AI doesn't just answer questions — it takes real actions like sending emails, booking meetings, or updating databases. This means a mistake isn't just a wrong answer; it's a real-world consequence that may be hard to undo. As a PM, your job is to figure out where to put guardrails so the agent can't cause damage before a human can catch it.

When you build an AI agent, don't give it the ability to do everything at once. Start by having it suggest actions to a human, then let the human approve them, then — only after you've confirmed it's usually right — let it act on its own for low-risk tasks. Think of it like hiring a new employee: you don't hand them the keys to the bank vault on day one.

Think of trusting an AI agent like trusting a new employee. You don't give them the keys to the building on day one — you give them small tasks, watch how they do, and gradually expand their responsibilities. AI agents work the same way: start with actions the user can undo, prove it works, then earn the right to do more.

When an AI just writes text, a mistake is just bad writing — easy to catch and ignore. When an AI takes actions in the world, a mistake can book 500 wrong meetings or send a mass email before anyone realizes. That is why agentic products need guardrails, human checkpoints, and the ability to undo actions before they become permanent.

When building an AI product that takes actions on behalf of users, start by picking one specific task where the outcome is measurable, mistakes are recoverable, and users can verify what the agent did. Don't try to automate everything at once. Prove the agent works on that one thing, show the value clearly, and only then consider adding more.