Group agents need new security, memory, and privacy playbooks

Single-user agents are solved; you can build one in an afternoon. The real shift is deploying agents in group settings—family chats, team workspaces, or always-on glasses. This changes everything about how you design security, memory, and privacy.

The security surface area explodes. Unlike an LLM where output inspection suffices, agents read emails, screenshots, GitHub issues, and group messages—all potential attack vectors. Static scans are insufficient; runtime attacks succeed 90% of the time. The solution is deterministic guards at the action surface, not the input.

Memory in groups isn't just storage—it's identity. The agent becomes what it remembers. Atomic fact extraction, continuous relevance scoring, and KV-cache-aware injection replace naive embedding-based approaches. Forgetting is as important as remembering; token costs and latency depend on it.

Privacy becomes a social contract problem. A grocery list is public; salary data is private—same agent, different context. The novel approach is baking permissions into model architecture via user-specific LoRA adapters rather than code-based access control.