Decagon builds AI agents that handle customer support conversations over chat, voice and email from one shared intelligence layer. Teams describe how an agent should behave using Agent Operating Procedures, written in natural language rather than a configuration language, then test, monitor and refine that behavior as the business changes. An analytics suite turns conversations into insight about what customers ask and need. Customer stories report results such as 70% chat and voice resolution, an 80% deflection rate and a 65% reduction in costs.
Decagon targets large consumer brands and enterprises with high support volume. Customer experience leaders use it to raise resolution rates without growing headcount, while operations managers and support engineers use the AOP approach to ship workflow changes without waiting on an engineering sprint or a vendor ticket. Voice, chat and email owners can keep a consistent experience across every channel, and analysts can study conversation trends to find gaps in products and policies.
Typical scenarios include a rental company extending a booking for the weekend, a retailer applying membership perks, a spa rebooking an appointment, or a financial brand resolving account questions by voice and chat. Teams build a workflow in plain language, validate it with testing and experimentation tools, deploy it to chat, voice and email at once, then watch observability dashboards and analytics to decide what to refine next. Decagon also serves companies replacing a previous vendor whose system demanded constant maintenance. Teams moving from a rules-heavy system often value that updates no longer depend on developers, and leaders can compare resolution, deflection and satisfaction figures across chat, voice and email to decide where to expand next.
