Nobody knows how to price for agents

I wrote recently about products losing their edges—how agents and open protocols dissolved the boundaries we used to draw around software. The question I didn’t get to: what does that mean for how SaaS companies make money?

Most companies frame this as a choice. Build proprietary AI to protect current revenue, or open the platform to external agents and risk becoming commodity infrastructure. The proprietary path is where the money is right now. Atlassian did it with Rovo, Salesforce built Agentforce, and it keeps per-user revenue up while using captive data as a moat.

Except the choice is false. Salesforce is doing both right now, shipping MCP support across the platform while launching a ChatGPT integration designed to head off customers building their own MCP connections. You can offer proprietary AI and open access at the product level. That part works fine. The pricing doesn’t.

Both paths break the seat model. Proprietary AI automates the work that justified seats in the first place—if your agent handles what three analysts used to do, you don’t renew three licenses. Open protocols do it faster. MCP hit Linux Foundation governance and broad adoption this year with 97 million monthly SDK downloads. Volume goes up but nobody’s sitting in a seat.

Bain analyzed 30-plus SaaS vendors and found 65% layering AI usage meters on top of seat pricing and 35% raising per-seat prices with bundled AI. The number that fully transitioned to outcome-based models: zero. Everyone’s hedging. Salesforce now runs three separate pricing models for Agentforce: per-conversation, per-action, and per-seat add-ons. That kind of confusion doesn’t happen when a company knows where it’s going.

What’s interesting is where the indecision creates openings.

Pricing is the obvious one. Incumbents can’t charge for outcomes without cannibalizing the seat revenue Wall Street expects, so they hedge. Sierra charges per resolved customer interaction and hit $100M ARR in 21 months. That model is nearly impossible to retrofit onto a seat-based business, and every incumbent that delays widens the gap.

Distribution might matter more. A protocol-compliant agent gets discovered by every MCP-enabled client without a sales call. Runlayer signed eight unicorns in four months selling MCP security this way. I’m watching whether the 12-month enterprise sales cycle starts working against the companies it was designed to protect.

The vertical question is the one I keep coming back to. Horizontal AI features are easy to replicate, but vertical agents that pass regulatory scrutiny in fields where a generic tool can’t operate are not. Incumbents spread across every use case consistently underinvest in any single domain. That seems like the most durable edge, but it’s early.

The window exists because incumbents are protecting seat revenue while the market moves past it. How long that lasts is worth paying attention to.