Voice AI Agents: Architecture, Deployment & Evaluation Best Practices
Blog post from Coval
Voice AI agents have evolved significantly by 2026, becoming integral production infrastructure across various industries like healthcare, financial services, and government. They handle thousands of calls daily, performing tasks such as transactions and scheduling, while updating downstream systems. The focus has shifted from merely building the agents to developing robust evaluation infrastructure that ensures their reliability and improvement over time. The architecture typically involves a five-layer stack—telephony, real-time transport, model stack, orchestration, and evaluation/observability—where weak links between layers can lead to failures. Enterprises often choose between cascaded pipelines, which offer superior observability, and speech-to-speech models, which excel in latency and natural interaction but lack transparency. Platforms like Vapi, Retell, LiveKit, and Pipecat are popular choices unless specific needs dictate custom builds. Successful deployments often start with narrow, high-volume use cases and emphasize thorough evaluation to close the gap between development and production performance. Compliance and cost considerations are crucial, with evaluation infrastructure being the key to maintaining quality and adaptability in dynamic environments.
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