AI agents are shifting from assistive tools to systems that act independently on behalf of users. They schedule meetings, make recommendations, trigger workflows, and increasingly execute decisions across systems. As this shift accelerates, the way users interact with software is fundamentally changing.
According to McKinsey's 2025 Global Survey on AI,62 percent of organizations are already experimenting with AI agents, yet Gartner predicts more than 40 percent of those projects will be canceled by 2027, not due to technical failure, but because users didn't trust or understand the systems.
Traditional UX research methods, built around direct interaction and task completion, are not designed to evaluate systems that act without continuous user input. This creates a gap between how AI agents behave and how their experiences are tested.
What Makes AI Agent Experiences Different
In traditional software, users initiate actions and systems respond. Control is explicit, and outcomes are directly tied to user input.
AI agents introduce a different interaction model. They interpret intent, make decisions, and execute tasks with limited visibility into their internal logic. This abstraction creates a distance between user intent and system behaviour.
As a result, users are not just evaluating whether a task was completed. They are evaluating whether the system behaved in a way that feels reliable, understandable, and aligned with their expectations.
One of the most critical challenges in AI agent UX is handling unexpected actions. Unlike traditional systems, where users directly initiate and validate each step, AI agents often operate with a degree of autonomy that can produce outcomes users did not explicitly anticipate.
When this happens, the issue is not only a functional failure. It becomes a breakdown in user trust and mental model alignment.
In many cases, users are not aware of all the conditions or logic the agent is using to make decisions. As a result, even technically correct outputs can feel incorrect from a user perspective if they do not match expectations or intent.
Research from the IBM Global AI Adoption Index 2023 highlights that trust and transparency are among the top barriers to enterprise AI scaling, particularly in systems where decision-making is not fully visible to users.
When unexpected behavior occurs, users typically respond in three ways:
They attempt to reverse or correct the action if control mechanisms exist.
They reduce reliance on the system over time if outcomes feel inconsistent.
Or they stop using the system altogether if unpredictability persists.
From a UX research perspective, this makes expectation alignment a core design requirement, not an afterthought. It is not enough to measure whether the system performs correctly. It is equally important to evaluate whether users can accurately predict what the system will do in a given context.
Addressing this requires a stronger emphasis on:
Without these safeguards, even high-performing AI agents risk being perceived as unreliable, not because they fail technically, but because they fail predictability.
Why Trust Becomes the Core UX Metric
Trust in AI systems directly influences adoption and long-term usage. In enterprise environments, this impact is even more pronounced, where decisions made by AI systems can affect operations, cost, and customer experience.
For AI agents, trust is shaped by predictability, transparency, control, and reliability. If users cannot understand or influence system behaviour, confidence declines quickly, even if outputs are technically accurate.
How to Test AI Agent Experiences
Testing AI agents requires moving beyond one-time usability sessions into more dynamic evaluation.
Simulation-based testing helps assess how agents behave across varied and unexpected scenarios. This is critical for identifying inconsistencies that do not appear in controlled environments.
Longitudinal testing provides insight into how trust evolves. Since AI agents are used repeatedly, measuring changes in user confidence across interactions is more meaningful than single-session feedback.
Decision path analysis further strengthens evaluation by examining how the system arrives at outcomes. Understanding these internal steps helps identify gaps in logic, transparency, and alignment with user expectations.
Common Gaps in AI Agent Experiences
As organizations deploy AI agents, several patterns continue to emerge.
Many systems lack visibility, leaving users unclear about how decisions are made. Others present outputs with high confidence, even when uncertainty exists. In some cases, users are unable to intervene or correct system actions, limiting their sense of control.
Inconsistent behaviour across similar scenarios further weakens trust. When the same input produces different outcomes, users begin to question the reliability of the system.
Improving AI agent UX requires deliberate design and research alignment.
Systems should provide clear explanations for actions, especially in high-impact scenarios. Users should be able to review, modify, or override actions where necessary, particularly in workflows that carry risk.
Designing for uncertainty is equally important. Communicating confidence levels and avoiding overly definitive outputs helps set realistic expectations. Standardizing system behaviour across similar contexts further improves predictability.
These changes strengthen user confidence without requiring fundamental changes to the underlying AI models.
The Road Ahead
As AI agents become more embedded in enterprise and consumer environments, expectations will continue to rise. Users will not just expect systems to function, but to behave in ways that are understandable and reliable.
Organizations that evolve their UX research practices alongside AI development will be better positioned to build systems that users trust and adopt at scale.
At Akraya, our UX Research practice specializes in evaluating AI agent experiences - from trust and transparency testing to longitudinal adoption studies. If your product is acting on behalf of users, your UX Research strategy needs to evolve with it. Let’s connect.