Building & Product

How should I price a product built on AI agents?

Per-seat pricing breaks when your product replaces labor instead of assisting it: agents don't have seats. The market is converging on usage and outcome pricing (Fin, formerly Intercom and now part of Salesforce, charges $0.99 per outcome; Zendesk bills per automated resolution; Salesforce charges $2 per conversation), usually wrapped in a hybrid: a platform fee plus variable consumption. Anchor your price to the value of completed work, agree contractually on what counts as an outcome (Fin bills a qualified lead at $9.99), and watch your token costs so an unlucky customer doesn't make you lose money per task.

Part of AI agents on eChai Explore.

On the AI map, 29 October 2026 Decision models Coding agents Agents that do tasks for you AI browsers No-code automation with AI steps

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More in AI Agents

What actually is an AI agent, in plain language? 7 → Why would a founder use agents instead of a chatbot or automation? 17 → What real tasks are founders delegating to agents today? 21 → How do I build my first agent without being technical? 10 → Which platform or framework should I build my agents on? 20 → How much do AI agents cost to run, and how do I keep the bill under control? 20 → How do I test an AI agent and know when it's reliable enough to trust? 17 → What can go wrong when an agent acts on my behalf, and how do I keep it safe? 19 → What is MCP, and why does it matter for my agents? 8 → Should I build one agent or a team of agents working together? 18 → Can coding agents like Claude Code actually build my product? 20 → Is there a real startup opportunity in building vertical AI agents? 19 → Why do most AI agent projects fail, and what do the winners do differently? 19 → How do I give my agent the right context and memory so it stops forgetting? 18 →

Also in Starting Up

The same ground, over in Build the product, our Starting Up track.

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