A Consent-First Personal AI Mentor Ecosystem: A Multi-Agent Framework for Lifelong Learning, Wellbeing, and Human Flourishing

Many of the most damaging trajectories in human life share a common root: no one was paying sustained, caring attention at the moments when small interventions would have mattered most. LLM agents make sustained, personalized attention technically feasible for the first time — but the obvious architecture, pervasive institutional monitoring, is both ethically unacceptable and counterproductive, because coerced observation destroys the trust that mentorship, disclosure, and behavior change depend on.

This paper proposes a consent-first alternative: every individual owns a Personal Mentor Agent (PMA) supporting their learning, health habits, and long-term goals, while institutions operate Institutional Coordinator Agents (ICAs) that can interact with a person’s PMA only through explicit, revocable, scoped consent.

Contributions

• A design analysis showing why consent-first architectures dominate surveillance architectures on effectiveness grounds, not only ethical ones.
• A reference architecture including an agent-to-agent consent protocol, a local-first personal data vault, and human-in-the-loop escalation for safety-critical events.
• Illustrative scenarios across education, healthcare, homes, and workplaces.
• A concrete evaluation plan with falsifiable hypotheses and measurable outcomes.

The central claim: consent is not a constraint on such systems, but the mechanism that makes them work.

Download the paper here

Citation

Kandula, C. R. (2026). "A Consent-First Personal AI Mentor Ecosystem: A Multi-Agent Framework for Lifelong Learning, Wellbeing, and Human Flourishing." Preprint.

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