AIエージェントは「育て方」と「追うKPI」で勝負が決まる──Decagon CEO Jesse Zhangが語る勝ち筋

Decagon CEO Jesse Zhang argues AI agent companies win or lose on training loops and KPIs, not the model itself.

• Oura Ring reduced human-escalation rate from 1-in-3 to 1-in-20 using Decagon’s agent training loop.
• The core pitch: non-engineers on the customer’s team must be able to train and improve the agent themselves, continuously.
• Key KPI to prove to customers is human-escalation rate, not resolution rate or CSAT alone.
• Post-sales motion for AI agent startups is longer and more expensive than traditional SaaS, requiring ongoing iteration.
• Decagon prices on labor-cost displacement: the baseline is what a human support headcount would cost.
• Moats in AI agents come from proprietary training feedback loops and customer-specific data, not the underlying model.
• Jesse advises against over-indexing on FDEs (forward-deployed engineers) as the improvement mechanism — the loop must be owned by the customer.

2026-02-16 · Watch on YouTube