Agentic Product Design
Building products where the product IS the agent, oversight, trust
1
Agentic Blast Radius Design
Agents act before you notice — own the tool list, own the blast radius.
An AI agent doesn't just answer questions — it takes real actions like sending emails or updating databases. If something goes wrong, the damage is already done before a human sees it. Your job as a PM is to decide which actions the agent is allowed to take, in what order, and when a human must approve before it proceeds.
2
Reversibility-First Trust Ladder
Reversible first, gate the irreversible, earn each rung.
When building a product powered by AI agents, always ask: can this action be undone? If the answer is no — like sending an email, processing a payment, or modifying a record — require a human to confirm before it happens. Start with the AI just making suggestions, and only give it more independence after you've confirmed it's consistently right.
3
Earned Agency Trust Ladder
Reversible first. Validate. Then earn the next rung.
When an AI does something for you — like sending an email or booking a meeting — you're trusting it not to mess up. But trust has to be built slowly. Start by letting the AI do small, reversible things under human supervision, confirm it works, then gradually let it do more. Never hand an AI full control before you've proven it deserves it.
4
Blast Radius Before Launch
Generative fails with words. Agentic fails with actions. Audit the tool list — that is the blast radius.
When an AI just writes text, a mistake means a bad answer you can ignore. When an AI takes actions — sending emails, updating records, scheduling meetings — a mistake means real-world damage that may be impossible to undo. You have to design for failure before you ship, not after.
5
Bounded Agentic Value Scope
Narrow scope, clear attribution, survivable failure — earn the next action.
An agentic AI product takes actions on behalf of users, not just answers questions. To do this safely and profitably, you need to pick a task narrow enough that when the agent gets it wrong, you can catch and fix it fast, and when it gets it right, the customer can clearly see the value it created. Start small, prove it works, then expand.