Beyond Generative: The Rise Of Agentic AI And User-Centric Design

Developing effective agentic AI requires a new research playbook. When systems plan, decide, and act on our behalf, UX moves beyond usability testing into the realm of trust, consent, and accountability. Victor Yocco outlines the research methods needed to design agentic AI systems responsibly.

• Victor Yocco
• Jan 22, 2026
• 0 comments

Beyond Generative: The Rise Of Agentic AI And User-Centric Design

• 19 min read
• UX,

Design,

• Share on Twitter, LinkedIn

About The Author

Victor Yocco, PhD, has over a decade of experience as a UX researcher and research director. He is currently affiliated with Allelo Design and is taking on …
More about
Victor ↬

*Weekly tips on front-end & UX.
Trusted by 182,000+ folks.*

Developing effective agentic AI requires a new research playbook. When systems plan, decide, and act on our behalf, UX moves beyond usability testing into the realm of trust, consent, and accountability. Victor Yocco outlines the research methods needed to design agentic AI systems responsibly.

Agentic AI stands ready to transform customer experience and operational efficiency, necessitating a new strategic approach from leadership. This evolution in artificial intelligence empowers systems to plan, execute, and persist in tasks, moving beyond simple recommendations to proactive action. For UX teams, product managers, and executives, understanding this shift is crucial for unlocking opportunities in innovation, streamlining workflows, and redefining how technology serves people.

It’s easy to confuse Agentic AI with Robotic Process Automation (RPA), which is technology that focuses on rules-based tasks performed on computers. The distinction lies in rigidity versus reasoning. RPA is excellent at following a strict script: if X happens, do Y. It mimics human hands. Agentic AI mimics human reasoning. It does not follow a linear script; it creates one.

Consider a recruiting workflow. An RPA bot can scan a resume and upload it to a database. It performs a repetitive task perfectly. An Agentic system looks at the resume, notices the candidate lists a specific certification, cross-references that with a new client requirement, and decides to draft a personalized outreach email highlighting that match. RPA executes a predefined plan; Agentic AI formulates the plan based on a goal. This autonomy separates agents from the predictive tools we have used for the last decade.

Another example is managing meeting conflicts. A predictive model integrated into your calendar might analyze your meeting schedule and the schedules of your colleagues. It could then suggest potential conflicts, such as two important meetings scheduled at the same time, or a meeting scheduled when a key participant is on vacation. It provides you with information and flags potential issues, but you are responsible for taking action.

An agentic AI, in the same scenario, would go beyond just suggesting conflicts to avoid. Upon identifying a conflict with a key participant, the agent could act by:

• Checking the availability of all necessary participants.
• Identifying alternative time slots that work for everyone.
• Sending out proposed new meeting invitations to all attendees.
• If the conflict is with an external participant, the agent could draft and send an email explaining the need to reschedule and offering alternative times.
• Updating your calendar and the calendars of your colleagues with the new meeting details once confirmed.

This agentic AI understands the goal (resolving the meeting conflict), plans the steps (checking availability, finding alternatives, sending invites), executes those steps, and persists until the conflict is resolved, all with minimal direct user intervention. This demonstrates the “agentic” difference: the system takes proactive steps for the user, rather than just providing information to the user.

Agentic AI systems understand a goal, plan a series of steps to achieve it, execute those steps, and even adapt if things go wrong. Think of it like a proactive digital assistant. The underlying technology often combines large language models (LLMs) for understanding and reasoning, with planning algorithms that break down complex tasks into manageable actions. These agents can interact with various tools, APIs, and even other AI models to accomplish their objectives, and critically, they can maintain a persistent state, meaning they remember previous actions and continue working towards a goal over time. This makes them fundamentally different from typical generative AI, which usually completes a single request and then resets.

A Simple Taxonomy of Agentic Behaviors

We can categorize agent behavior into four distinct modes of autonomy. While these often look like a progression, they function as independent operating modes. A user might trust an agent to act autonomously for scheduling, but keep it in “suggestion mode” for financial transactions.

We derived these levels by adapting industry standards for autonomous vehicles (SAE levels) to digital user experience contexts.

Observe-and-Suggest

The agent functions as a monitor. It analyzes data streams and flags anomalies or opportunities, but takes zero action.

Differentiation
Unlike the next level, the agent generates no complex plan. It points to a problem.

Example
A DevOps agent notices a server CPU spike and alerts the on-call engineer. It does not know how or attempt to fix it, but it knows something is wrong.

Implications for design and oversight
At this level, design and oversight should prioritize clear, non-intrusive notifications and a well-defined process for users to act on suggestions. The focus is on empowering the user with timely and relevant information without taking control. UX practitioners should focus on making suggestions clear and easy to understand, while product managers need to ensure the system provides value without overwhelming the user.

Plan-and-Propose

The agent identifies a goal and generates a multi-step strategy to achieve it. It presents the full plan for human review.

Differentiation
The agent acts as a strategist. It does not execute; it waits for approval on the entire approach.

Example
The same DevOps agent notices the CPU spike, analyzes the logs, and proposes a remediation plan:

• Spin up two extra instances.
• Restart the load balancer.
• Archive old logs.

The human reviews the logic and clicks “Approve Plan”.

Implications for design and oversight
For agents that plan and propose, design must ensure the proposed plans are easily understandable and that users have intuitive ways to modify or reject them. Oversight is crucial in monitoring the quality of proposals and the agent’s planning logic. UX practitioners should design clear visualizations of the proposed plans, and product managers must establish clear review and approval workflows.

Act-with-Confirmation

The agent completes all preparation work and places the final action in a staged state. It effectively holds the door open, waiting for a nod.

Differentiation
This differs from “Plan-and-Propose” because the work is already done and staged. It reduces friction. The user confirms the outcome, not the strategy.

Example
A recruiting agent drafts five interview invitations, finds open times on calendars, and creates the calendar events. It presents a “Send All” button. The user provides the final authorization to trigger the external action.

[...]

Original source