How to build an AI agent that remembers returning customers

Twilio outlines how AI customer-service agents can retain context across repeat interactions by combining identity resolution, relevant context recall, and write-back of new information. The approach links identifiers such as phone numbers, emails, and chat IDs to unified customer profiles, stores persistent traits, extracted observations, and conversation summaries, and retrieves useful memories before an agent responds across voice, SMS, WhatsApp, RCS, and chat. It recommends explicitly configuring memory retrieval and profile grouping, manually saving urgent changes during live conversations because automatic extraction occurs only after a conversation becomes inactive or closes, and passing profile and memory data during handoffs to human agents in Twilio Flex. The guidance responds to research indicating that many customers must repeat themselves to AI systems and may abandon interactions when not recognized, while emphasizing privacy-conscious use of past context, confirmation of changeable information, and mechanisms for customers to correct or delete inaccurate memories.

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