What I learned building voice agents at Decagon | Decagon

This post details the significant engineering effort and process improvements required to scale voice agent development at Decagon. It highlights the transition from manual, time-consuming development of individual voice agents to a more systematic, productized approach. Key contributions include: transforming tribal knowledge into accessible playbooks for Agent Development Managers (ADMs), productizing voice customization and multilingual setup to reduce engineering dependency, and integrating advanced voice simulations (Duet simulations) with Duet Autopilot for faster testing and iteration. The post quantifies the impact with a ~37% reduction in time-to-go-live and more than halving of engineering hours per launch, enabling ADMs to launch voice agents with minimal agent engineer involvement.
More from the Voice Activity Detection (VAD) and Turn Detection trail
- Audio-native semantic speaker-change detection | Decagon
- Why speech-to-speech models aren’t ready for the enterprise (yet) | Decagon
- "Agent, agent, agent": designing conversations to reduce barge | Decagon
- Conversational IVR from definition to business impact | Decagon
- Beyond latency: The art of building a truly great voice agent | Decagon