Building AI Agents That Work in Group Chats

Group chats are presented as a primary product surface for social, coordination, concierge, commerce, and community agents rather than merely expanded one-to-one conversations, because they allow agents to introduce participants, organize logistics, manage planning tasks, and step back while people interact. Linq’s Partner v3 API supports iMessage and RCS group chats with creation through multi-recipient messages, group display names and icons, participant management for iMessage groups, and webhooks for chat and membership changes, though initial messages cannot contain links, recipient limits vary by delivery channel, groups must retain at least three members, and group chats lack typing, delivery, and read receipts. The central engineering challenge is determining when an agent should participate, since replying to every message can be disruptive while inconsistent silence damages trust. Linq recommends separating this decision from response generation through a two-stage architecture: a small, inexpensive classifier evaluates each group message as respond, react, or ignore, while a more capable model generates content only after the classifier selects a response or reaction. This routing approach can reduce cost and latency, improve consistency, prevent reaction loops, and allow teams to independently tune participation behavior. Effective group agents also need sender-attributed conversation history and group-specific prompting so they can recognize who is speaking, address participants appropriately, keep replies brief, and account for the visibility of every response.

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