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We reframe user interviews as assumption-killing tools that reduce risk and reveal real behavior. We walk through desirability, viability, and feasibility, then show how to design hypotheses, ask better questions, avoid bias, and synthesize patterns that matter.

• reframing interviews from validation to risk reduction
• the big three risks desirability, viability, feasibility
• pipeline from belief to hypothesis to question
• behavior over opinions as the core signal
• four evidence types past, present workflow, emotion, decision criteria
• direct vs indirect questions and when to use each
• avoiding leading language, hypotheticals, and founder energy bias
• synthesis signals repeated pain, workarounds, delay, resistance
• designing interviews that expose gaps rather than soothe egos

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We trace the shift from passive tools to proactive AI agents and show why design now means building trust, boundaries, and clear goals. From memory controls to uncertainty cues, we map the guardrails that turn raw capability into a reliable collaborator.

• hammer versus helper framing of agents
• autonomy, adaptability, goal orientation as pillars
• risks of fluent but unreliable systems
• designer’s role as steering, brakes, and safety
• memory scope, retention limits, and a forget button
• designing for uncertainty, clarification, and escalation
• moving from prompt tweaks to goal architecture
• becoming a pilot, not a passenger

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