How Agentic AI Reimagines User Journeys: A Psychological Framework

2. The Principle of Mental Models and Explainable Agency

The ability to use any product relies on our having a mental model of the product—our internal, often subconscious understanding of how something works. Traditional software is relatively easy to model. But an agentic AI, with its independent decision-making, can be a black box. This opaqueness is a major hurdle to user adoption. To overcome this hurdle, do the following:

• Make the invisible visible. A new user journey must include meta-tasks—actions and notifications that reveal the agent’s thought process. This is explainable agency. For example, an agent could observe that a user frequently reorders the same groceries on a weekly basis, then proactively suggest, “I’ve noticed that you often reorder these items. Would you like me to automatically add them to your cart for your approval?”
• Design for codiscovery. Instead of the user’s discovering features, the human and the agent can codiscover and refine the optimal workflow together. The journey is less about a fixed path and more about a guided exploration. For UX researchers, a modified Wizard of Oz (WOZ) testing method is invaluable here. By having a human researcher secretly simulate the AI’s autonomous actions, you can test a user’s comfort level and mental models in a low-fidelity environment. This research approach helps pinpoint exactly where the user expects the AI to show its work, where they feel surprised by its autonomy, and how they build their mental model of the agent over time through codiscovery. Let’s consider the example of a creative professional using an AI design assistant. Instead of the assistant simply generating predefined mood boards, the human and AI collaborate. The human provides initial stylistic preferences and project goals—for example, modern, minimalistic, for a technology startup. The AI then presents a range of visual concepts, not just as finished products, but as adaptable starting points. The human might highlight certain elements from different concepts—“I like the color palette from this one, but the typography from that one.” The AI then learns from these selections, refining its understanding and offering new variations that blend these preferences. This iterative process of codiscovery enables both the human’s creative intuition and the AI’s processing power to discover optimal design solutions together.
• Use familiar metaphors. To build an easy-to-understand mental model, we should leverage psychological metaphors that humans already understand. We could frame an agent as a personal assistant, a concierge, or a collaborator to help users immediately grasp its role and capabilities. Let’s look at the example of a user planning a trip with the assistance of agentic AI. Instead of a generic search engine, the user interacts with an AI travel concierge that understands his preferences, suggests personalized itineraries, books flights and hotels, and even offers real-time recommendations during the journey—much as a human concierge would.

3. The Principle of Goal Alignment and Reinforcement

In psychology, goals drive behaviors, which are reinforced by rewards. In a traditional user journey, the goal is often a single, transactional outcome—for example, booking a flight. With agentic AI, the goal might be a more complex, multistep objective—for example, planning an entire trip. The journey must be designed to align the human’s long-term goals with the agent’s autonomous actions. To enable collaboration, do the following:

• Define shared goals. The first step in a human-agent journey should be a clear, shared, goal-setting activity. The system must confirm the user’s intent and desired outcome before taking any action. For product designers, this means defining a Goal Specification Template in your documentation. For every complex goal the user assigns to the agent—for example, Plan an entire trip—the specification must clearly state key milestone triggers when an achievement occurs and the associated reinforcement mechanisms—that is, the notifications, animations, or visual changes that provide the psychological reward. This makes celebrating milestones a deliberate design requirement, not an afterthought.
• Provide progress feedback. Humans are motivated by a sense of progress. The user journey should include frequent, visible feedback on the agent’s progress toward the goal. This could be a simple status bar or a summary of completed tasks.
• Celebrate milestones. Reinforce the human-agent partnership by celebrating key milestones. A notification such as “Your travel plans are now 80% complete” or “Agent Alpha has successfully booked all your flights and hotels” provides a psychological reward, strengthening the user’s trust and sense of accomplishment.

Conclusion: The Future Is a Partnership

Agentic AI is moving us from a world of tool usage to a world of partnerships. As UX researchers and designers, our role is no longer just about optimizing paths; it’s about building relationships. The most successful agentic AI systems will not only be effective but also psychologically sound—empowering users while respecting their need for control, understanding, and clear goal alignment.

Agentic AI is reshaping the user journey, transitioning from the paradigm of tools to one of collaborative partners. This shift demands rethinking our roles as UX researchers and designers. No longer is our primary objective solely about optimizing predefined paths or streamlining series of steps. Instead, our focus must now pivot toward cultivating robust, meaningful relationships between users and AI systems.

The most successful agentic AI systems will transcend functional efficacy. Their true distinction will lie in their psychological soundness. Achieving this entails designing systems that profoundly empower users. We must balance empowerment with a respect for core human psychological needs—for example, the inherent need for control over one’s actions and outcomes, a clear understanding of how systems operate and make decisions, and an unambiguous alignment of goals between the user and the AI. When all of these elements are thoughtfully integrated, agentic AI can move beyond being a sophisticated instrument to becoming a trusted, valuable, and indispensable partner in achieving the user’s aspirations.

The responsibility for designing this human-agent relationship belongs to us. We must champion the psychological principles of control, transparency, and alignment to ensure that the agentic future is not just automated, but truly human-centered.