Decagon
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Enterprise AI support agents across voice, chat, and email, defined in natural-language Agent Operating Procedures rather than flow charts.
Overview
Decagon direct competitor is Sierra, and the differentiator is how agents are authored: Agent Operating Procedures let a support ops lead write policy in natural language instead of maintaining a decision tree, which collapses the iteration loop from an engineering ticket to an edit. The rest of the platform is built around trusting that loop — A/B testing, QA simulations, and Watchtower for continuous quality monitoring, plus Voice of the Customer analysis on the conversation corpus. Named customers include Chime, Duolingo, ClassPass, Rippling, American Airlines, and Riot Games, with vendor-reported outcomes like 70% resolution at Chime and 80% deflection at Duolingo. Read those as vendor-reported. Evaluate Decagon against Sierra on two axes: who owns agent authoring in your org, and whether you want outcome-based or conventional pricing.
Pros & Cons
Pros
- Agent Operating Procedures let ops teams author behavior in plain language
- Omnichannel voice, chat, and email from one platform
- Built-in A/B testing, QA simulations, and continuous quality monitoring
- Strong reference logos in consumer fintech and travel
Cons
- No published pricing
- Vendor-reported deflection numbers are unaudited
- Natural-language procedures still need rigorous testing — plain language hides ambiguity
- Enterprise-scale product; wrong fit for SMB support volumes
Workflows that use Decagon
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