Wearing the Agent: Engineering a Family-and-Friends Personal Agent, from Group Chats to Glasses

Official Schedule Context

Judith is a personal AI agent that has run in daily production for a year, used by more than a dozen

of my family and friends across three WhatsApp group chats, Telegram, and Discord. This talk walks

through how it's built, in two parts. The first part is the engineering that makes one agent safe

for many people to share: a multi-tenant permission model (read-only for my mom, exec for me), a

memory stack — FAISS + Neo4j + curated long-term notes — that stays useful over a year instead of

bloating into noise, cron-scheduled subagents that scout and act on their own, and the guardrails it

enforces on every message — redact personal info before posting to a group, never reply to the wrong

person, and screen attacker-controllable text for prompt injection before acting on it. The second

part takes the agent off the screen and onto a $50 pair of smart glasses. It captures what I see,

describes and stores it as a running visual memory, sets destination path on maps before I get onto

car, finds and tells me which aisle in the store to go to first, etc. I cover the latency budget

that keeps it conversational — on-device Whisper for speech, cloud reasoning, sub-one-second round

trips — and the custom neural voice it speaks in rather than stock TTS, drawn from my speech-

synthesis background. Both parts are shown live, including a candid look at the pieces that don't

work yet. Audience takeaways: A multi-tenant architecture for a personal agent multiple people

actually share A memory design that survives real long-term use (not just a vector store) A

defensive checklist for any agent that ingests untrusted text A blueprint for an ambient, vision-

aware wearable interface on commodity hardware, with a real latency budget

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