Turning Agents into Products
Find problems worth solving, price agent products, and design UX that earns user trust.
You can now build an agent, make it safe, deploy it, and keep it working. The remaining question is the one that decides whether any of that matters to anyone but you: will people use it, and will they pay?
Agent products fail for different reasons than ordinary software. The demo is easy and impressive, so it's tempting to build before checking whether the problem is worth solving. Costs vary per task, so the wrong pricing model can lose money on your best customers. And users are rightly wary of software that acts on their behalf, so an agent that works but doesn't feel trustworthy goes unused. This lesson covers how to find problems worth solving, how to price agent products without guessing, and how to design UX that earns trust step by step.
Finding problems worth solving
The best agent products don't start from "what can agents do?" They start from work that people already do, already dislike, and already pay for. A useful test is to score a candidate problem on six questions:
| Question | Strong signal | Weak signal |
|---|---|---|
| How often does it happen? | Daily or weekly, for many people | A few times a year |
| How painful is it? | People complain about it unprompted | "It's fine, I guess" |
| Is someone already paying for it? | Staff time, contractors, or software budget goes to it today | Nobody spends money on it now |
| Can success be checked? | Clear right answers, or quick human review | Quality is subjective and slow to judge |
| What does a mistake cost? | Errors are caught easily and are cheap to fix | Errors are silent and expensive |
| Can you get the data and access? | The needed systems have APIs or exports | Information lives in people's heads or locked systems |
Problems that score well across the board are things like reconciling invoices against purchase orders, triaging and drafting replies to support tickets, preparing first drafts of routine documents from structured inputs, or keeping a CRM up to date from email. They're unglamorous, which is exactly why they're open.
Two patterns show up again and again in successful agent products:
- Narrow beats general. "An AI assistant for small businesses" is hard to sell and hard to make good. "Turns supplier invoices into reconciled ledger entries for restaurants" tells a buyer exactly what they get, and lets you build specialized tools, prompts, and evals that a general assistant can't match.
- Sell the outcome, not the agent. Customers don't want an agent; they want the inbox triaged, the report written, the backlog cleared. Describe your product in terms of the work that disappears.