Generative and agentic AI are changing how people search, shop, request support, and use digital services, according to Adobe's 2026 AI and Digital Trends Report. As voice-first assistants begin to compare options, users need to know what the system understands and how much authority they have given away.

Val Pavliuchenko, a product, UX, and UI designer with experience across future-facing digital products, argues that the next generation of AI assistants will be judged by whether people can read the product's behavior. As Founder and CEO of Hosanna Studio, he leads a product design agency and creates visual identity, product interfaces, and the interaction language through which digital products communicate with users. Global corporations such as Google and Apple have applied Val's interface concepts, UX structures, and visual systems to turn complex product ideas into clear, navigable, and understandable digital experiences.

For much of their history, companies built voice assistants for narrow, predictable requests. A user asked for a timer, a song, a call, or a light switch, and the system either completed the task or failed visibly enough for the person to try again. Generative AI has made that exchange less linear. As Val explains, "Voice-first assistants can now help plan a trip, narrow a purchase, handle a support question, draft content, or move information between tools. Its role now extends into interpreting intent, weighing options, and moving closer to decisions that once belonged entirely to the user."

Val encountered this problem directly in his work on Brain AI and CoStar, where he worked with voice input and chat-based output. In CoStar, the Copilot mode was built around a more autonomous interaction model: a user could give the system a task by voice, leave it to work, and return to a completed result, such as a long document prepared and formatted in a file. The same agentic logic could extend to everyday tasks, from calling a restaurant to book a table to buying a plane ticket or ordering room service. Val's work in this area helps address one of the central product challenges of agentic AI: making autonomous systems understandable, controllable, and useful to ordinary users.

The design challenge was that users did not always know how much the system could do. A person might ask it to find a nearby hotel, while the model could also book that hotel if the request was framed correctly. Under Val's direction, the team addressed this through the interface itself, using ready prompts and on-screen cues to show what the agent could handle, how the user could ask, and where delegation required approval.

That detail changes the usual discussion around voice AI. Speech may feel natural, but in agentic products, it does not automatically explain the product's capabilities, limits, or next steps. In CoStar, Val developed user scenarios, screens, the design system, visual language, prototypes, chat logic, and the presentation of prompt results so the user could move from request to outcome without losing track of what the assistant understood, what it had taken over, and what it had produced.

In Val's view, design is not limited to visual polish or a polished conversation tone: "A good design is the way a product explains itself through structure, cues, and repeated interaction patterns. In a voice-first AI product, those patterns have to answer practical questions before the user becomes confused. What can this agent do? How should I ask? Is it searching, preparing, calling, booking, or generating? What will I receive when the task is complete?" he says.

Voice is useful at the start of an interaction because it lets people state intent quickly. But voice alone cannot carry the full weight of delegation. When an AI assistant searches for a flight, prepares a payment, or orders a service, the user still needs to see the terms, the status, and the point of approval. This is where Val's work stands out. He treats the interface not as decoration around the voice layer, but as the system that makes the assistant's actions readable. In his approach, design defines what the agent can do, what it is doing now, and where the user remains in control. That is a more serious product problem than giving an assistant a warmer tone or a more conversational script. A pleasant voice can make a system feel approachable, but only a well-designed interface can show whether the assistant is suggesting, waiting, acting, or asking for permission before an action becomes final.

For Val, the more important question is whether the product behaves consistently across the full experience: "If an assistant asks for confirmation before a small task but moves too quickly through a sensitive one, trust breaks. If a mobile app, a smart speaker, a car interface, and a web dashboard all present the same agent in different ways, the user experiences fragmentation rather than intelligence. A coherent product language gives the assistant recognizable rules across those surfaces."

The CoStar example shows why this matters. When an agent can take over routine tasks, the value is time. Val compares it to the way a dishwasher or washing machine removes repetitive work from daily life. For AI to deliver that kind of relief, users have to feel that delegation is controlled rather than mysterious. They need enough guidance to ask properly and enough feedback to understand the result.

As generative and agentic AI move deeper into search, shopping, support, and everyday digital services, Val Pavliuchenko's unique approach can help companies close the gap between technical capability and user trust. By treating voice-first AI as a full interaction system that teaches users how to ask, shows what the agent can handle, and presents results in a form that can be checked or corrected, product teams can turn autonomous assistance into a reliable tool rather than an impressive but unclear feature.