agent-era-pricing

# Agent Era Pricing Skill Seat pricing quietly assumed the user was a human who logs in. Agents break the assumption from both sides: your customers need fewer seats (one operator, ten agents), and your product gets *more* usage than ever. This skill redesigns the model around a value metric that survives non-human users — without torching existing revenue on the way. ## What This Skill Produces - A **value-metric decision**: what you charge for when seats stop proxying value - **Agent-tier design**: how agent/API usage is packaged, fenced, and priced - **Cannibalisation math**: what happens to current revenue under the new model, computed on real cohorts - A **phased migration plan** for existing customers, with the grandfathering decision made explicitly ## Required Inputs Ask for (if not already provided): - **Current model**: plans, price points, seat definitions, current API/automation pricing if any - **The evidence of pressure**: seat contraction, API traffic growth, customer asks, competitor moves - **Unit economics**: cost to serve a seat vs an API call/agent action (rough is fine, labelled) - **3-5 representative customer profiles** with seat counts and usage (the cannibalisation test set) ## Method 1. **Find the value metric that survives agents.** Test candidates against three questions: does it scale with the value the *customer* receives (not your costs)? · is it counted identically whether a human or agent drives it? · can the customer predict their bill...