Impact of Agentic AI on UX Research & Design

Before we get into the grit of how Agentic AI impacts user experience and user behaviors on the internet, lets first discuss what the heck Agentic AI is.

What is Agentic AI?

Agentic AI refers to artificial intelligence systems that can autonomously take actions, make decisions, and execute tasks with minimal human intervention. Unlike traditional AI models that respond passively to user inputs, agentic AI can plan, execute, adapt, and optimize based on dynamic environments and goals. These AI agents often use large language models (LLMs), reinforcement learning, and multi-step reasoning to complete tasks independently.

Key Characteristics of Agentic AI:

✨ Autonomy: Can perform complex workflows or decisions without requiring constant human input.

✨ Goal-Oriented Behavior: Works toward achieving specific objectives set by users or predefined policies.

✨ Adaptability: Can learn from interactions and adjust behaviors over time.

✨ Multi-Step Execution: Can break tasks into sub-tasks and complete them in a structured manner.

✨ Context Awareness: Understands past interactions and can maintain memory for long-term reasoning.

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So how does that impact the work we do in UX and UI worlds?

What is the Impact of Agentic AI on UX Research & Design?

Agentic AI fundamentally changes how users interact with digital systems. This shift impacts interaction models, trust, user expectations, and usability principles in several ways.

UX Research Implications

First, user expectations are going to shift from control to oversight. Users are no longer just operating tools but supervising AI agents that make autonomous decisions. Researchers will need to study how much autonomy users are comfortable with and how much control they want for any given experience.

We already hear this all the time when it comes to AI in general, but trust and transparency are going to become even more critical. Users must trust AI decisions, meaning explainability and ethical considerations will become core research topics.

Lastly, we are going to see a shift in mental models of interaction and the need to reinforce the education and onboarding of users into this new way of thinking. People may struggle to understand how and why the AI takes certain actions, requiring new ways of designing mental models for AI behavior.

UX Design Implications

We're going to see a spike in growth for conversational and task-based interfaces. I was recently speaking about this new user experience for LLMs with "infinite canvas" UX and this is just the beginning. Agentic AI shifts interfaces from static UIs to more adaptive, proactive, and conversational experiences.

Understanding how to design for predictability, and interact with ongoing activities, will also become more critical to the user experience. Since AI can act independently, users must be able to override, modify, or halt actions if necessary.

Lastly, and some what related to my first point, is that we are going to see new interaction patterns emerging. How exciting is it to get to define a new user behavior? We are setting the foundation of the future and what users will ultimately find comfortable and familiar with AI patterns right now! Traditional click-based UIs may give way to things like natural language interactions, voice interfaces, and agent-driven automation.

Digging Deeper into Agentic AI Patterns That Affect User Behavior

This is where things get juicy! As agentic AI systems become mainstream, UX professionals should watch for emerging interaction patterns that reshape user behavior.

#1 Autonomous Task Completion

AI agents proactively complete multi-step workflows, from scheduling meetings to summarizing long documents, fundamentally shifting the user’s role from task execution to task review. This mirrors the evolution we've already seen with autocorrect and AI-generated content—where users move from manual effort to refinement and approval.

🤓 Applying this concept to UX – designing for trust and oversight will be crucial. Users will need previews, approval mechanisms, and rollback options to ensure AI-driven automation aligns with their expectations. After all, if an AI assistant books the wrong flight, the first question won’t be “how fast was that?” but “why did it do that?”

#2 AI Collaboration & Delegation

AI is evolving beyond a simple tool—it’s becoming a co-pilot, assisting users by offering suggestions, automating decisions, and handling tasks autonomously. The user experience is shifting from execution mode (doing) to collaboration mode (reviewing, refining, and approving).

🤓 Applying this concept to UX – it’s about designing seamless feedback loops that ensure AI actions are transparent and easily editable. Users should always have the ability to refine outputs, override decisions, or ask why the AI made a particular choice. The more intuitive the back-and-forth, the more natural AI collaboration becomes.

#3 Context-Aware Personalization

AI is no longer personalizing experiences based on pre-set rules; it’s dynamically adapting in real-time based on user behavior and context. The result? A shift from static settings to a fluid, responsive, and hyper-personalized experience that feels intuitive rather than configured.

🤓 Applying this concept to UX – personalization shouldn’t feel like a black box. Users must have clear, accessible controls to modify AI-driven adjustments. If AI auto-adjusts preferences in the background, users should know why and have the option to tweak or reset them. Nobody wants to feel like their experience is changing without their input.

#4 Autonomous Discovery & Recommendations

AI agents are stepping in as digital curators, surfacing relevant content, suggestions, and opportunities before users even think to ask. Whether it’s intelligent search, real-time recommendations, or proactive alerts, AI is redefining how and when information reaches users.

🤓 Applying this concept to UX – the challenge isn’t just surfacing recommendations, it’s ensuring they are timely, relevant, and non-intrusive. Users should feel guided, not bombarded by AI-suggested content. A well-designed experience aligns recommendations with user intent rather than overwhelming them with excessive noise.

#5 Continuous Learning & Adaptation

AI remembers past interactions and adapts to behaviors over time, making it a critical component of a growing, evolving, and intuitive user experience. Because of this, users will expect AI to understand their preferences and anticipate their needs without redundant inputs. Personalization becomes significantly easier—and more seamless—with Agentic AI.

🤓 Applying this concept to UX – we must offer transparency in AI adaptation, ensuring users can see how and why AI learns. Providing an option to reset AI learning is just as important as fine-tuning it. Users will undoubtedly think, “I wish the AI had learned this differently”—so we should design experiences that let them recalibrate AI behavior when needed.

Final Thoughts

Agentic AI represents a paradigm shift in UX. Instead of designing static interfaces, UX professionals will design experiences that evolve dynamically with user needs. Success in UX for agentic AI will require:

✨ Designing for Explainability: Users need to understand what the AI is doing and why.

✨ Creating Trust & Transparency: Users must feel confident in AI decisions.

✨ Ensuring Control & Oversight: Users should have the ability to override AI actions.

✨ New Mental Models for Interaction: Traditional UI principles need to evolve to support agent-driven, conversational, and adaptive experiences.

By staying ahead of these changes, UX researchers and designers can shape a future where agentic AI enhances rather than disrupts human experiences.