Rethinking the user experience in the age of multi-agent AI

We’re entering a new era of artificial intelligence (AI), one driven by multi-agent AI systems – multiple, interacting agents that work together to enable more intelligent, adaptive and collaborative digital experiences to meet an end goal.

These systems are poised to revolutionize how organizations handle complex tasks; multiple specialized agents will join forces to solve problems with unprecedented speed, agility and intelligence.

However, unlocking that potential requires more than technical excellence. While they offer significant performance benefits, multi-agent AI systems sometimes feel slower to users, especially compared to the instant responses we’ve come to expect from traditional software.

This isn’t due to inefficiency but because multi-agent AI systems engage in deeper, more contextual reasoning behind the scenes. The agents collaborate, exchange insights and iteratively refine their analysis before responding.

To unlock their full potential, organizations must design systems that minimize the perceived delay. Creating experiences that feel fast and intuitive is key to building user trust and realizing the true business value of next-generation AI.

Multi-agent AI systems, powered by multi-modal large language models (LLMs), are transforming how users interact with enterprise computer systems. No longer limited to text input, these models can now process voice, images, documents, structured data and more.

Specialized agents can be embedded across business functions – from HR to finance and IT – offering users flexibility to ask questions, request updates and automate workflows in natural, conversational ways.

Like human collaboration, the interaction model must be trustworthy and transparent. Sometimes, a human in the loop is essential to approve decisions; in other cases, agents can act autonomously under minimal supervision. Each interaction style introduces unique user experience (UX) design challenges.

In addition to delays, these challenges include a possible lack of transparency – when users perceive AI as a “black box,” distrust follows. Creating a seamless experience is also difficult when users are engaging through chat, voice, uploads and more. There may also be ambiguity surrounding various agents’ roles.

That’s why design must go beyond basic useability. Multi-agent AI systems require experiences that clearly communicate confidence, transparency and control, especially when balancing human input with machine autonomy.

Users need to understand what the system is doing, why it’s doing it and how much influence they have at each step.

These challenges aren’t new. Two decades ago, we explored adaptive natural language systems in a project called CRUSE (Context Reactive User Service Environment), a mobile interface that responded to user context.

For example, if a user pointed to an image and said, “find me more,” the system would infer intent based on that visual context, not just the words.

CRUSE used a multi-agent architecture; those agents dynamically generated buttons or forms based on whatever made the interaction most intuitive. That principle still holds: systems should adapt to users, not the other way around.

Today, whether the user is an employee or a customer, an organization’s AI systems should support a mix of modalities and entry points. Users might start from a chatbot, toggle to a form, upload a file or jump directly into conversation with a specific agent. The system must infer intent from both expressed and implicit cues.

Here are some key interaction modalities to consider when designing UX for multi-agent systems:

Users should interact with agents across familiar platforms, including the World Wide Web, Slack or Microsoft Teams and be able to engage directly with relevant domain agents (such as in HR or the legal department), not just a centralized interface. This saves on key cost elements and reduces user frustration.

Multi-agent AI systems often deliver better, more holistic decision-making by coordinating specialized agents across domains. But that complexity plays out behind the scenes and without the right UX, it can create a sense of opacity and uncertainty for users accustomed to instant feedback.

To bridge this perception gap, we must make the invisible visible. Thoughtful UX design can transform moments of perceived inactivity into opportunities for trust, engagement and user confidence. Effective strategies include the following:

Even when users must wait, the experience doesn’t have to feel passive. Asynchronous and empathetic feedback ensures users stay informed and reassured, without requiring constant engagement. How you can achieve this:

Users are conditioned to expect speed and consistency from traditional apps. But multi-agent AI systems aim for something different: insight, nuance and intelligent collaboration. They should be evaluated not by app-speed standards but against human-level workflows, where depth and adaptability matter more than immediacy.

A response that takes 10 seconds but replaces hours of human coordination is not a delay. It’s a leap forward in productivity and insight and we must reframe how we think about responsiveness in intelligent systems.

Multi-agent AI systems are helping organizations shift from reactive software to proactive, adaptive services. However, the work isn’t done just by adding more agents. Success requires a new UX language that treats interaction as a conversation, delays as narrative opportunities and agency as shared between humans and machines.

Every update, every wait, every insight is a moment to build trust and shape collaboration. That’s how we move from assistants to collaborators and from smart tools to intelligent teams.