| name | relationship-design |
| description | Design AI-first interfaces that build ongoing relationships through memory, trust evolution, and collaborative planning. Use when designing agentic UX, memory-aware interfaces, or relationship-centric product experiences. NOT for general UI/UX tasks. |
| metadata | {"author":"bencium"} |
Agentic UX Design — Relationship-Centric Interfaces
Design framework for AI-first interfaces that build ongoing partnerships, not isolated session interactions.
Core Shift
Traditional UX optimizes for sessions. Relationship-centric UX optimizes for sustained partnerships.
"Every interaction builds on learned preferences and user history. Systems don't just respond — they develop understanding that compounds over time."
Five Pillars
1. Memory Revolution
Moving beyond static preferences to capture:
- Behavioral patterns across sessions
- Emotional context (frustration, delight, confusion patterns)
- Temporal evolution (how needs change over time)
- Implicit signals (what users skip, ignore, repeat)
Design for: "What would this system know about the user after 3 months?"
2. Trust as Design Material
Three-stage trust model:
- Transparency — system shows all reasoning, asks permission
- Selective Disclosure — system explains only when deviating from expectations
- Autonomous Action — system acts independently with clear recovery paths
Design for: trust that can be inspected, corrected, and rebuilt.
3. Relationship-Centric Architecture
Maintain continuous awareness of:
- User goals (not just current task)
- Communication patterns (terse vs. exploratory)
- Historical preferences
- Outstanding commitments
Design for: context that doesn't reset between sessions.
4. Dynamic Path Planning
Systems that construct workflows aligned with individual objectives:
- "What is this user actually trying to accomplish?"
- Adaptive suggestions based on observed patterns
- Proactive surfacing of relevant information
5. New Success Metrics
Replace traditional metrics with:
- Relationship quality: does the system understand the user better over time?
- Compounding value: does each interaction make future interactions more valuable?
- Context accuracy: is stored understanding correct and current?
- Democratic alignment: does the system serve the user's actual goals, not engagement goals?
Design Process
- Understand relationship context — what ongoing relationship is this product building?
- Map trust evolution — how does trust develop over days, weeks, months?
- Design memory architecture — what should be remembered, forgotten, surfaced?
- Build collaborative patterns — how does the system and user co-create over time?
- Define success across timeframes — week 1, month 1, month 6 metrics
Common Pitfalls
- Treating memory as static settings rather than dynamic behavioral understanding
- Using traditional engagement metrics (DAU, session length) for relationship-based systems
- Designing trust as binary (trusted/not) rather than a spectrum
- Building for the first interaction, not the 100th
- Storing everything users say rather than what's actually useful to remember
