Experience Research and Metrics for AI Agents

Experience research and experience measurement are core challenges that every product must face. With the development of agentic AI, products are shifting from "passive tools" to "active collaborative partners." User experience has also evolved from interface operations to the holistic process of human‑AI collaboration in task completion. This transformation has made traditional user‑centered research methods and satisfaction‑centric measurement systems increasingly inadequate. Enterprises urgently need a set of experience methods and evaluation frameworks tailored to the agentic era.

This workshop will help participants systematically understand how to conduct experience research and measurement for agentic products, apply them in product design and business advancement through a systematic approach, and ultimately improve the experience of agentic products and services.

Main Content:

1. Opening
1.1 Speaker background introduction
1.2 Topic introduction: workshop objectives and significance
1.3 Workshop agenda and grouping instructions

2. Analysis of Limitations of Traditional Experience Research and Measurement Methods
2.1 Review of traditional user research methods
2.2 Deconstruction of issues in traditional experience measurement dimensions
2.3 New challenges in experience research and measurement for agents

3. Construction of a Human‑AI System Experience Research Framework and Measurement System
3.1 The essence of agent research: from user research to human‑AI system research
3.2 Development of experience measurement metrics for agents
3.3 Closed‑loop system for agent experience measurement

4. Group Hands‑On Exercise – Building an Experience Indicator System for Agentic Agents
4.1 Select a scenario and deconstruct agentic agent experience issues
4.2 Design experience indicators based on scenario characteristics and business needs using a six‑dimensional framework
4.3 Intra‑group review and refinement of indicators

5. Group Presentations and Feedback
5.1 Groups present their agent experience indicator system for a chosen scenario, explaining innovation and feasibility
5.2 Instructor feedback and optimization, guiding deeper application of knowledge

6. Summary, Review, and Outlook
6.1 Recap of core knowledge, reinforcing systematic understanding
6.2 Participants share learning takeaways and application plans
6.3 Outlook on trends in agent experience research and measurement

1、Opening
2、Theoretical sharing: agent experience research and measurement systems
3、Group practice and discussion
4、Presentation of case outcomes and feedback
5、Summary

1、User experience designers
2、Experience researchers
3、AI product managers / agentic product designers

1、Obtain an experience research framework and measurement system tailored to the agentic era.
2、Master the key experience drivers for agentic products, clearly identify the core factors affecting agent experience (cognition, trust, collaboration, growth, etc.), help teams identify optimization priorities in complex AI scenarios, and enhance product competitiveness.
3、Elevate the business value and decision‑making impact of experience work — transform experience from subjective perception into quantifiable metrics, enabling experience results to directly support product evaluation, priority determination, and business decisions.