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

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

Skip to main content Start reading the article Jump to list of all articles Jump to all topics19 min readUX, Design, AIShare on Twitter, LinkedInAbout The AuthorVictor Yocco, PhD, is a UX Researcher at ServiceNow and the author of Design for the Mind (Manning, 2016) and the forthcoming Designing Agentic AI Experiences … More about Victor ↬Email NewsletterYour (smashing) email Weekly tips on front-end & UX.Trusted by 182,000+ folks. See User Testing Live Smart Interface Design Patterns, 45 lessons + UX training Custom Web Forms for Angular, React, & Vue. Your backend. Celebrating 10 million developers Advertise on Smashing Magazine Naming Design Systems with Samantha Gordashko How To Measure UX and Design Impact with Vitaly FriedmanDeveloping 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 BehaviorsWe 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-SuggestThe agent functions as a monitor. It analyzes data streams and flags anomalies or opportunities, but takes zero action.DifferentiationUnlike the next level, the agent generates no complex plan. It points to a problem.ExampleA 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 oversightAt 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-ProposeThe agent identifies a goal and generates a multi-step strategy to achieve it. It presents the full plan for human review.DifferentiationThe agent acts as a strategist. It does not execute; it waits for approval on the entire approach.ExampleThe 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 oversightFor 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-ConfirmationThe agent completes all preparation work and places the final action in a staged state. It effectively holds the door open, waiting for a nod.DifferentiationThis differs from “Plan-and-Propose” because the work is already done and staged. It reduces friction. The user confirms the outcome, not the strategy.ExampleA 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.Implications for design and oversightWhen agents act with confirmation, the design should provide transparent and concise summaries of the intended action, clearly outlining potential consequences. Oversight needs to verify that the confirmation process is robust and that users are not being asked to blindly approve actions. UX practitioners should design confirmation prompts that are clear and provide all necessary information, and product managers should prioritize a robust audit trail for all confirmed actions.Act-AutonomouslyThe agent executes tasks independently within defined boundaries.DifferentiationThe user reviews the history of actions, not the actions themselves.ExampleThe recruiting agent sees a conflict, moves the interview to a backup slot, updates the candidate, and notifies the hiring manager. The human only sees a notification: Interview rescheduled to Tuesday.Implications for design and oversightFor autonomous agents, the design needs to establish clear pre-approved boundaries and provide robust monitoring tools. Oversight requires continuous evaluation of the agent’s performance within these boundaries, a critical need for robust logging, clear override mechanisms, and user-defined kill switches to maintain user control and trust. UX practitioners should focus on designing effective dashboards for monitoring autonomous agent behavior, and product managers must ensure clear governance and ethical guidelines are in place.Figure 1: The Agentic Autonomy Matrix. This framework maps four distinct operating modes by correlating the level of agent initiative against the required amount of human intervention. (Large preview)Let’s look at a real-world application in HR technology to see these modes in action. Consider an “Interview Coordination Agent” designed to handle the logistics of hiring.In Suggest ModeThe agent notices an interviewer is double-booked. It highlights the conflict on the recruiter’s dashboard: “Warning: Sarah is double-booked for the 2 PM interview.”In Plan ModeThe agent analyzes Sarah’s calendar and the candidate’s availability. It presents a solution: “I recommend moving the interview to Thursday at 10 AM. This requires moving Sarah’s 1:1 with her manager.” The recruiter reviews this logic.In Confirmation ModeThe agent drafts the emails to the candidate and the manager. It populates the calendar invites. The recruiter sees a summary: “Ready to reschedule to Thursday. Send updates?” The recruiter clicks “Confirm.”In Autonomous ModeThe agent handles the conflict instantly. It respects a pre-set rule: “Always prioritize candidate inter