Decagon

The interesting thing about Decagon isn't the chatbot. It's the operating model: write the procedure in plain English, test it in Simulations, A/B it in Experiments, monitor it in Watchtower. Most enterprise agent programs stall because changing agent behavior requires a release cycle. Decagon's pitch is that it doesn't. Pick it when you're a large brand with real contact volume across more than one channel. Voice plus chat plus email under one intelligence layer, with memory that survives the channel switch, is the scenario where this architecture pays off. The published 70% chat and voice resolution and 80% deflection numbers are vendor-selected, but they're specific and tied to named customers, which is more than most competitors offer. Pass when you're small. There's no published pricing, no free tier, and no self-serve signup—everything routes through a demo request. If you're a startup handling a few hundred tickets a month, that sales cycle alone is a mismatch, and a lighter tool will get you further faster. Also pass if your differentiation depends on custom agent logic you want to own end-to-end. Decagon's value is the managed lifecycle; if you'd rather build on raw model APIs and control every prompt, you're paying for scaffolding you'll fight. Against Zendesk AI or Intercom Fin, the tradeoff is breadth. Those tools live inside an existing helpdesk stack and are easy to switch on. Decagon asks for a bigger commitment—it wants to be the intelligence layer, not a feature inside someone else's. In practice that means more omnichannel consistency, and more organizational buy-in to get there. One caveat worth flagging: the pace of launches here is fast—Browser Actions, in-platform collaboration, and Decagon Assist all landed within roughly a quarter. That's