Building a Multi-Channel AI Agent with Twilio Conversations and eve

Twilio’s tutorial explains how to build a multi-channel AI customer-support agent that handles SMS and WhatsApp through Twilio Conversations while using Vercel’s eve framework for durable agent sessions. The architecture separates short-term conversation transcripts managed by eve from long-term customer profiles, traits, summaries, and automatically extracted observations stored in Twilio Memory Store, allowing the agent to retain relevant context across channels and future interactions. It walks through creating an eve project, configuring an LLM provider, exposing a local webhook through ngrok, provisioning Twilio Memory Store and Conversation Orchestrator resources, and setting GROUP_BY_PROFILE to unify a customer’s SMS and WhatsApp identities. The guide then develops a custom Orchestrator-aware eve channel that validates JSON webhooks, filters outbound-message echoes, links cross-channel customer identifiers, routes each reply through the correct channel, and retrieves stored customer information through a dynamic system instruction. It also describes testing the setup, inspecting extracted memory in the Twilio Console, aligning conversation and session timeouts, and extending the agent to voice or richer interactive message formats.

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