Building a Multi-Agent Voice Command Center: How We Scaled 8 AI Agents on Supabase Edge Functions and VAPI

说明与用途

使用 Supabase Edge Functions、Vercel、Postgres 和 VAPI 构建了可扩展的语音自动化平台，实现 8 个 AI 代理和 8 个工作流的并发运行，无需专用服务器。

问题

Deploying a production voice automation platform that can run 8 concurrent AI agents, 8 automation workflows, and process real‑time webhooks without dedicated servers.

结果

The architecture supports 8 concurrent AI agents and 8 workflows, processes real‑time webhooks, and eliminates the need for dedicated servers, achieving scalable voice automation.

工作流

• 1. Built a 5‑page frontend on Vercel for user interaction.
• 2. Designed an 8‑table Postgres schema to store agent state and workflow data.
• 3. Integrated Asterisk PBX with VAPI for voice orchestration.
• 4. Deployed Supabase Edge Functions to route, scale, and persist multi‑agent conversations.
• 5. Configured real‑time webhook processing within the Edge Functions.

证据

We just shipped the Quiet Hours Valet Command Center—a production voice automation platform handling 8 concurrent AI agents, 8 automation workflows, and real‑time webhook processing through Supabase Edge Functions.

来源

This post breaks down the architecture we built: a 5-page frontend on Vercel, an 8-table Postgres schema for agent state management, Asterisk PBX integration with VAPI for voice orchestration, and the Edge Function patterns that let us route, scale, and persist multi-agent conversations without spinning up dedicated servers.

来源