The AI is changing fast from being able to simply answer questions. Today, we have AI agents that can schedule meetings, manage workflows, screen applicants, and much more.
With increasing autonomy of the AI system comes a very important question. That is, who will be responsible if the AI makes a decision? Most companies currently deal with this by having audit logs, explanations of how the decision was made, and controls to allow a human to stop the system from taking any further action. Although many of these tools help ensure regulatory compliance and provide necessary data to regulators, most are developed for regulatory bodies rather than for the end users of the products.
Transparency in AI is a design constraint that ultimately affects whether users trust the AI.
The biggest issue for users is determining exactly what the AI agent is doing at this exact moment, and whether that process can be trusted. That lack of trust is starting to affect businesses negatively.
According to a study published by MIT, almost 95 percent of generative AI pilots in enterprises do not produce significant results. This highlights the difference in capabilities of AI and actual implementation into day-to-day activities.
In systems where an AI acts independently (agentic) and performs tasks over time (i.e., workflow processes across various systems and timelines), transparency should be viewed as a fundamental component of the overall user experience.
Why Developing Products That Utilize Agentic AI is More Difficult Than Developing Traditional AI Products
Agentic AI systems differ significantly from traditional AI systems, such as chatbots or recommendation engines. Instead of providing a single response to a user’s prompt or presenting options based upon pre-existing criteria, agentic systems execute several actions across workflows, systems, and timelines on a user’s behalf. In many cases, the user may not need to intervene at each step of the process.
For example, an AI agent could:
- Search for available meeting times
- Narrow down those options
- Schedule a meeting
- Send out invitations to attend the meeting
- Start the next workflow related to preparing materials for the meeting
- Continue to complete other decisions required before attending the meeting
The success of each task depends upon previous successes.
Since agentic AI systems execute multiple tasks and perform actions over extended periods of time, there is a distinct difference in the type of User Experience (UX) challenges presented compared to developing a traditional AI application.
With the use of AI agents, a user’s request initiates a long series of AI activities before the system provides its results. Thus, the gap between initiating and completing an activity is where a user develops either confidence or distrust of the system.
To provide good service (i.e., create trust), there are three major issues that surface:
1. Invisible Activities of AI
In most cases, AI agents have taken many invisible actions (e.g., searching databases) prior to responding to a user’s question. Most interfaces handle this by using a spinning circle (“loading”) as evidence that some work is being done. Some interfaces will indicate to the user “we’re working on it”, but rarely does the interface show enough about the process of how the answer was generated.
2. Multiple Steps in AI Decision-Making
AI systems typically generate multiple related decisions from one input. Showing the final answer, but not explaining the reasoning behind the answers, leaves the user missing essential context required to determine if the system’s decision was accurate and trustworthy.
3. Accountability in Agent-Based Systems
As systems become more complex and incorporate multiple agents and external tools, each component begins to pass tasks off to others. Each time a task is passed from one component to another, it becomes harder to track who did what based on their individual logic, thus making accountability even less possible than in simpler systems.
Providing transparency means implementing an entire accountability layer, including interaction patterns, decision visibility, and user controls, so that users know what is going on at all times, and therefore build trust with, and guide the AI.
Also Read - Agentic AI UX Design: 5 UX Patterns That Work
Transparency as a Design System: The Accountability Layer
A well-implemented accountability layer consists of four primary components:
1. Legibility: How Users Understand Important AI Activity
Users do not want to know everything that happens internally during a session. If too much information is provided, users experience cognitive overload and lose trust. Rather than attempting to inform the user of every step within the internal workings of an AI system, the true design challenge lies in identifying:
- Which important decisions should be exposed to users?
- At which point(s) should a user see reasoning behind those decisions?
- What level of detail regarding those decisions would be useful for users?
One of the most successful approaches has been to expose reasoning at branches (the point at which the AI chooses among several options). For instance, as an AI agent assists a recruiter reviewing applicant profiles, it can state: “I have identified 47 matching applicants and selected 12 applicants that match my recent activity criteria.”
The user will then have sufficient understanding of how the AI arrived at its conclusions, but not so much detail as to overwhelm.
2. Interruption: User Ability to Halt or Review AI Activity
An agentic AI system needs to provide users with the ability to halt or review activity prior to completion. The mechanism of doing so cannot be considered an emergency button. Thus, these mechanisms should be integrated seamlessly throughout the product experience.
The largest hurdle will be determining what level of autonomy should be granted to AI systems. In other words, when do we want our systems to operate autonomously, and when do we want them to request approval?
There is a fine line between allowing automation to become overly burdensome and requesting approvals too frequently, versus allowing the AI to function independently, thus removing the visibility and control of the user. Therefore, reversibility will be critical in balancing both factors.
One way to create a balance is to develop a mapping of the actions of your AI system relative to:
- Stakes
- Reversibility
- Consequences
Using the above elements, you can categorize actions. For example:
- Information Retrieval (low stakes) requires no interruption.
- Communication Drafts (moderate stakes) require preview approval.
- Booking Meetings/Purchases (high stakes) requires user confirmation.
| Action Type |
Stakes |
Reversibility |
Design Pattern |
| Information retrieval |
Low |
N/A |
Silent, log only |
| Recommendations or Prioritization |
Medium |
High |
Show criteria, allow adjustment |
| Draft communication |
Medium-High |
Moderate |
Preview before send |
| External communication or Execution |
High |
Low |
Explicit approval required |
| External system action |
High |
Low/None |
Confirmation with consequences shown |
3. Attribution: The Visibility of Ownership
The user should be able to understand two things when an agentic AI system provides results:
- What decisions were made by the AI
- What decisions were approved (or not) by the user
Clarity between these two distinctions is important for both accountability and trust.
Attribution design focuses on creating visual and structural differences between actions generated by the AI and actions confirmed by humans. Additionally, designers must create decision trails that clearly illustrate the key choices the AI made based on the signals used to make them. It’s important to avoid creating an audit log that looks technical. Instead, designers should present a simple view of how the outcome was reached from a human perspective.
An example would include an AI agent assisting a hiring team in shortlisting candidates and mapping: “Filtered active candidates → sorted by match score → excluded candidates already in pipeline → drafted outreach based on role brief.”
This will allow the user to understand the reason behind the result without revealing any unnecessary technical complexity.
4. Consent Architecture: Designing Ongoing Permission
Typically, most agentic AI products obtain initial consent during onboarding through screens asking for permissions, settings, and checkboxes defining what the AI can and cannot do. However, there are limitations to this approach.
Users typically have to decide about important aspects before they really understand how the product works. Also, consent is treated as a one-time setup step rather than an ongoing relationship between the user and the AI system.
A better consent architecture is incremental and contextual. Unlike requesting all permissions at once, the AI should ask for approval when it first wants to take a new action or higher-stakes action, such as sending a message, accessing a new data source, or taking an action with significant consequences
In the case of an AI assistant working alongside a recruiter in candidate outreach, an AI agent might ask: “Are you ready to see the message I’ve drafted, so you can review it before I send it?”
The purpose of asking for permission to perform the next step is to allow the human decision-maker to continue to guide the process while enabling the AI agent to execute the function. This enables users to know what the AI agent is going to do. This builds confidence and trust in the system and provides the user control over the final decision.
Also Read: What are Multimodal Interfaces? A Complete Guide [2026]
When Should Agentic AI Explain Its Reasoning?
One of the most significant user experience challenges in creating agentic AI is determining whether a system should provide feedback about its logic when executing tasks and how much detail should be provided.
AI systems should provide reasoning when:
- A decision was made through some form of subjective judgment
- An action has significant consequences, cannot be reversed, or is difficult to recover from
- A new activity is taking place for which there is no prior precedent
AI systems do not need to provide feedback when:
- Routine tasks are performed automatically and are considered safe or low risk
- Actions taken by an AI system are easily reversible
- Providing explanations for actions taken would add unnecessary complexity
Also Read: What is Zero UI Design? UX Without Screens Explained
Why Transparency and Accountability Matter Across Every Agentic AI Industry
Transparency and accountability are important for all industries utilizing autonomous AI agents. Let’s see how agentic AI systems are being applied across multiple industries:
- Finance: AI agents are used to manage portfolios, categorize expenses, detect potential fraudulent transactions, and recommend investments. Users require transparency regarding how decisions are prioritized and executed.
- Healthcare: AI systems can now schedule appointments, facilitate patient communications, navigate patient care pathways, and coordinate clinical workflow activities. Transparency is important where decisions may impact patient satisfaction and quality of care.
- Customer Service: AI agents are utilized to resolve tickets, route escalated requests, and automate response messages. Both customers and customer service personnel require clarity as to how issues are being categorized and resolved.
- Legal Operations: AI systems aid in reviewing contracts, identifying potential risks, summarizing large documents, and assisting legal operations with compliance workflows. Legal teams must understand what the AI has identified, what it did not identify, and why particular recommendations were made.
- Retail/E-commerce: AI agents can help in offering inventory recommendations, delivering personalized shopper experiences, adjusting pricing based on demand, and enabling automated consumer interactions. Companies need consumers to trust how their recommendations and decisions are being produced.
For all of these industries, the fundamental UX problem remains. That is, when an AI agent takes an action, do users understand what occurred, why it occurred, and how to intervene, if desired?
Unfortunately, answers remain ambiguous today. The rate of technological advancement surrounding agentic AI currently exceeds the development of corresponding interaction design models required to produce systems that are understandable, trustworthy, and accountable.
Why Trust and Transparency are Critical Components to Developing Successful Agentic AI Experiences
Companies developing agentic AI products are primarily competing on speed, automation, and novel functionality. The largest differentiator for companies competing with one another in terms of trust will be the ability to instill trust within their users.
Users are not attracted solely by the power of an AI agent. Instead, they use them when they understand how the system functions, why it makes certain decisions, and how much control they retain over the ultimate result.
When users lack confidence in an AI system, they typically limit access (permissions), disregard recommendations, or automate processes. Conversely, users tend to significantly increase their use of an AI product when they can:
- See how the AI arrived at its conclusion
- View specific actions taken by the AI
- Modify any actions taken by the AI when appropriate
- Customize their degree of autonomy relative to their comfort level
Therefore, transparency in agentic AI is fundamentally a UX and product design issue. Building trust among users cannot occur through policy extensions or technical documentation related to explanation. Rather, it occurs as part of providing a transparent layer of accountability throughout the user’s overall experience with the product.
Design Agentic AI Systems Users Can Trust
Agentic AI systems are becoming more autonomous, but autonomy alone does not build trust. Users need to understand what an AI system is doing, why it is making certain decisions, and when they can intervene.
With increased adoption of autonomous AI agents in hiring, finance, healthcare, customer service, and other mission-critical business areas, success will depend upon those organizations that design their agentic AI experiences as both highly transparent and highly trustworthy.
At Onething Design, we work with companies that are developing agentic AI experiences and want to balance automation with transparency, accountability, and user trust. If you are building an agentic AI product and exploring how to make the experience more transparent and user-friendly, we would be happy to help. Feel free to get in touch with our team to discuss your product and design challenges.