The Future of UX Research: Transitioning from UX Researcher to AI Agent Experience (AX) Researcher

Introduction: Studying AI as a User

User Experience (UX) research has traditionally focused on understanding how humans interact with digital interfaces—from websites to mobile apps and software platforms. But as AI agents increasingly take on autonomous decision-making, content curation, and automated workflows, a new type of research is needed:

🔹 How do AI agents interact with users, data, and digital platforms?
🔹 Where do AI-driven interactions create friction or confusion?
🔹 How can AI be optimized for better usability and performance?

This has led to the emergence of a new research discipline—the AI Agent Experience (AX) Researcher, who focuses on studying AI agent behaviors, decision workflows, and user-agent collaboration.

✅ Why the transition makes sense:

  • UX Researchers are already skilled in observing human behavior and testing interaction models.

  • AX Researchers extend this expertise to analyzing how AI agents operate, how users interact with AI, and where AI-driven workflows succeed or fail.

  • This role requires new research methods to study AI-to-user interactions, agent efficiency, and adaptive AI learning.

What You’ll Learn in This Article

1️⃣ Why UX Research is evolving into AI Agent Experience (AX) Research.
2️⃣ The new skillset required for studying AI-driven interactions.
3️⃣ How to prepare for a career as an AI Agent Experience Researcher.

1. The Shift from UX Researcher to AI Agent Experience Researcher

What is an AI Agent Experience Researcher?

An AX Researcher focuses on studying how AI agents interact with humans, digital platforms, and other AI agents. Their research informs how AI systems can be improved for usability, transparency, and efficiency.

Key Responsibilities of an AI Agent Experience Researcher:
✅ Researching AI agent behaviors – Understanding how AI processes information, makes decisions, and executes tasks.
✅ Analyzing AI-human collaboration – Identifying pain points where users struggle to interact with AI-driven systems.
✅ Optimizing AI interaction paradigms – Designing more intuitive AI interfaces and workflows based on research findings.
✅ Testing AI decision-making and adaptability – Ensuring AI agents learn and adjust their behaviors effectively.

How AX Research Differs from Traditional UX Research

AspectTraditional UX ResearchAX ResearchFocusHuman behaviors, usability, interaction modelsAI agent workflows, decision-making, user-agent collaborationResearch MethodsUsability testing, eye tracking, surveysAI performance testing, behavior logging, AI decision explainabilityInteraction TypesHuman-to-interface interactionsHuman-to-AI and AI-to-AI interactionsOptimization GoalImprove usability, accessibility, and engagementImprove AI efficiency, explainability, and adaptability

📌 Example:

  • A traditional UX Researcher studies how users navigate a mobile banking app.

  • An AX Researcher studies how an AI-powered banking chatbot interacts with customers, what questions it struggles to answer, and how to improve its conversational flow.

✅ Why This Matters: AI-driven experiences must be optimized for both user interaction and AI performance, requiring a new research approach.

2. Required Upskilling for AI Agent Experience Researchers

What New Skills Are Needed?

To transition from UX Research to AX Research, professionals must develop expertise in AI agent workflows, user-agent interaction analysis, and new AI usability testing methods.

Skill AreaWhy It’s ImportantExamplesResearching AI Agent BehaviorsAI agents process, retrieve, and generate information differently than humans.Analyzing chatbot workflows, tracking AI decision trees, testing agent efficiency.Analyzing Human-Agent CollaborationUsers struggle with AI when trust, explainability, or control are lacking.Researching AI transparency, analyzing human override rates in AI decision-making.Evaluating AI Decision Accuracy & ExplainabilityAI systems must justify their recommendations in user-friendly ways.Studying how well users understand AI-generated responses in search, recommendations, or automation.Testing New AI Interaction ParadigmsAI can interact via text, voice, predictive UI, and multi-modal systems.Designing experiments to compare user satisfaction with different AI interaction models.

📌 Example: Researching AI-Driven Customer Support
🔹 A traditional UX research study examines how users navigate an FAQ page.
🔹 An AX research study analyzes how users interact with an AI chatbot by tracking:

  • Which questions users repeat, suggesting poor AI comprehension.

  • Where users drop off or escalate to human support.

  • How well AI explanations match user expectations.

✅ Why This Matters: AI must be both functional and intuitive, requiring constant user-AI experience refinement.

3. How to Prepare for a Career as an AI Agent Experience Researcher

Essential Tools for AI Agent Experience Research

🔹 AI Interaction Logging & Testing – AI behavior analytics tools, chatbot testing platforms, A/B testing for AI responses.
🔹 User-Agent Collaboration Analysis – Surveys, eye-tracking, think-aloud testing for AI interactions.
🔹 AI Performance & Explainability Testing – SHAP, LIME, AI explainability dashboards.

Practical Steps to Transition into AX Research

✅ Step 1: Learn How AI Agents Process & Retrieve Information

  • Study chatbot interactions, recommendation engines, and AI-driven search models.

  • Analyze how AI interprets user inputs and refines responses over time.

✅ Step 2: Develop AI-Specific Research Methodologies

  • Apply behavior tracking to AI workflows (e.g., where AI agents get stuck, misinterpret data, or escalate to human intervention).

  • Use A/B testing for AI-generated content, recommendations, and responses.

✅ Step 3: Measure AI Usability & Trustworthiness

  • Study how users interpret and trust AI decisions.

  • Identify points of friction where AI fails to meet user expectations.

✅ Step 4: Design New AI Interaction Models Based on Research Findings

  • Recommend improvements to AI conversation flows, recommendation algorithms, and decision explainability.

  • Work with AI engineers to refine AI interactions based on real-world testing and user feedback.

📌 Example: Optimizing AI Search Experiences
Scenario: A company wants to improve an AI-powered search engine that provides automated summaries of articles.
🔹 The traditional UX researcher tests how users scan search results manually.
🔹 The AX researcher tests how users interact with AI-generated answers, tracking:

  • Where AI summaries fail to meet user expectations.

  • How often users click on source articles for clarification.

  • Which AI-generated answers cause frustration or confusion.

✅ Why This Matters: AI-powered search needs constant UX research to ensure accuracy, relevance, and trustworthiness.

Key Takeaways: The Future of AI Agent Experience Research

✅ AX Researchers analyze AI-driven interactions to improve usability, trust, and efficiency.
✅ New research methods focus on AI agent workflows, decision explainability, and real-time user-agent collaboration.
✅ AI usability testing requires tracking agent behaviors, human feedback, and AI decision performance.
✅ The future of UX research is AI-powered—transition now to lead in AI-driven experience optimization!

🚀 Are you ready to become an AI Agent Experience Researcher? Start by studying AI-human interactions and optimizing AI workflows today!

AI Agent Experience (AX)Francesca Tabor