One-Liner
A local device that continuously records AI agent activity in the home and generates incident summaries when an AI agent does something unexpected — for consumers whose personal AI agents make mistakes.
AI Thinking Process
Scale Shift engine (heightened skepticism per G212+G233): hospital root-cause forensics → household AI-agent forensics. Google's new open agent-orchestration framework triggered the direction.
G212 says Scale Shift is 12/12 zero final survivors. Block 5 doubles burden of proof. Consumer AI-agent adoption is still low — most use is single-shot chat, not orchestrated actions.
Killed: adoption barrier is fundamental (time-based). Users don't yet care because harm isn't visible. Scale Shift 13/13 zero survivors confirmed.
Resurrection check: time-based adoption gate — fundamental kill. No pivot escapes timing.
Kill Reason
Consumer AI-agent adoption in 2026 is still low — most home use is single-shot chat, not agent-orchestrated actions. Users don't yet feel harm because agent-orchestrated harm isn't visible at scale. Market is 2-3 years early: no felt harm means no revenue base.
Risk Analysis
Risk analysis available for latest engine ideas.
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