The AI agents market is expected to hit $50 billion by 2030, growing at 46 percent each year. When we think about how this is going to impact everyday interactions with technology, most people jump straight to what AI interfaces will look like. Will there be more voice commands, gestures or maybe even holograms?
But we’re probably spending too much time thinking about the interface itself. That’s not where the real transformation is happening.
The real breakthrough will be AI’s ability to interpret what you want across different inputs and then pull information and functionality from different sites, apps and tools to deliver the outcome in whatever format you prefer. Whether that’s a voice response, text summary, image or map directions depends on what makes sense for your request and context. The interface becomes more dynamic, adaptive, and personal when this coordination happens smoothly in the background. For designers, this means shifting focus from individual screens to understanding how AI interprets context and coordinates across systems.
A truly intelligent assistant helps with the everyday, without prompting. For example, suppose you have an afternoon meeting across town. Your AI quietly checks for traffic delays, suggests the best departure time, books a ride if public transport is disrupted and prepares a summary of the client’s latest updates from your inbox. It even reminds you if your laptop battery is low so you’ll remember to bring your charger. This level of support is about intelligence that works unprompted in the background and isn’t about what device, app or input you use.
How Are AI Agents Changing Design Principles?
Designing for AI agents requires shifting focus from individual screens to background orchestration. Key design principles include:
- Contextual Intelligence: Anticipating needs without prompts (e.g., checking traffic before a meeting).
- Multimodal Input: Allowing users to switch between voice, text and gestures seamlessly.
- Invisible Orchestration: Managing connections between specialized agents and APIs in the background.
- User Control: Providing clear mechanisms to interrupt, correct, or cancel AI actions.
Conversations Replace Commands
Moving from menus to conversations changes everything about how we design. When users express what they need through whichever input feels natural in the moment, designers must create systems that understand intent immediately and deliver outcomes without friction.
Let’s consider two different interactions focused on ordering takeout. Traditionally, you open an app, search locations, filter by cuisine and time, select options and confirm payment across several screens. With an intelligent assistant, you could simply state what you want or tell it that you took a photo of a dish you enjoyed last week. The system figures out which food you mean based on your history, checks availability across platforms in relation to your location and completes the booking and transaction, all without additional prompting. You just have to confirm the suggestion that it serves up.
Today, instructing LLMs might still require a little bit of legwork, but the potential is already there ChatGPT is already showing hints of this. Tell it you’re a developer, and the interface adapts to include functionality you actually need and use on a day-to-day basis. Based on the information you give it about your role, it can even deliver its output in your preferred programming language and tailor that output to your specified style.
As it develops further, agentic AI should be designed to understand intent without that prerequisite legwork, then handle connecting to different services and tools behind the scenes. “Orchestration agents” can organize the work and coordination between other service or function-specific agents. The goal here is to alleviate the need for the user to manually switch between apps, with the agent figuring out which tools it needs and managing those connections for you.
To do this, we need to design good agent-to-agent interfaces. Stable APIs or Model Context Protocol (MCP) servers that can ensure smooth communication between and across different services. This means establishing standardized data formats and authentication protocols that allow agents to share information and coordinate actions securely.
For instance, when you ask your AI to “book dinner and update my calendar,” the orchestration agent needs to communicate with a restaurant booking service, verify your preferences from a separate profile service and then write to your calendar application — all while maintaining data consistency and user privacy across these different systems.
But humans will never see these technical interfaces. So, to make using these agents a great experience for humans, designers must account for all interaction modes users prefer while making sure the “master AI agent” delivers the desired outcome in the desired modality. This could be tapping buttons, using voice, looking at something with smart glasses, pointing your phone or even gestures. The system must smoothly handle this variability without the user experiencing friction.
Control matters too. Users need to know that they can interrupt, correct, or cancel AI actions at any moment, and control how much they’re willing to share. Designing this control means mapping out not just happy paths, but understanding how AI systems can fail and building in clear correction mechanisms.
Accessibility Determines Adoption
Even sophisticated AI becomes useless when users can’t reliably access it. Intelligence must work properly under everyday constraints, not just in controlled demos on premium hardware. It must be able to handle challenges such as spotty connectivity, offline environments and less robust devices.
The solution is to design AI that works well even when conditions aren’t perfect. Core features need to function with weak internet or none at all. AI systems should cache common requests and user preferences locally, allowing basic operations to continue offline. When connectivity returns, the system syncs seamlessly. Additionally, designing with “AI-on-the-edge” architectures — where lighter models run directly on devices, not on cloud servers — keeps essential features working no matter the network quality. Different devices have different capabilities, so the system should adapt, using simpler and faster processing when needed.
After all, AI assistants that work only on flagship phones connected to high-speed networks will reach limited audiences instead of the global mainstream.
We also have to consider accessibility beyond the technology. Multimodal interactions introduce complexity, especially when considering those with sensory impairments, mobility issues or diverse language needs. The goal isn’t forcing users into new patterns; they need to be able to choose what feels natural to them and have the system meet them where they are.
Trust Beats Novelty
All of this plays into the trust that’s built between the user and the agent. Flashy interfaces and futuristic interactions attract attention but rarely keep it.
Rather than teaching users new gestures or commands to use with their technology, companies need to build systems smart enough to interpret ambiguous requests correctly the first time. When users must repeat themselves or clarify repeatedly, trust erodes. They go back to doing things manually, just like you would fire a human assistant who never gets your instructions right.
But when the AI remembers your preferences and habits, adapts to your patterns and improves its responses over time, users develop confidence in the system. This requires accurate, contextual data that the AI can use to make decisions and pull from the right sources. For designers, this means working closely with data teams to identify which signals matter most. Recent behavior often outweighs older patterns, and explicit preferences should override implicit ones.
And of course, the AI isn’t going to get it right every time, not for a while anyway. So users need to understand why the system made particular choices, especially for decisions with consequences. This transparency means that users correct course when the AI misinterprets their intent, which again helps them build trust with their agents.
Designing the Invisible Intelligence
What’s clear is that AI won’t replace the interface as we know it. But it will change its nature and our expectations. The future will be defined by questions around how agents develop: anticipating people’s needs, acting within the right context, tailoring to individuals and completing tasks across tools, all without exposing a patchwork of interfaces.
Making this possible means designers must understand system context, data flows and how AI models reason through decisions. The skills and work for designers need to change, including learning how to write prompts that shape AI behavior, understanding how language models prioritize and process information and mapping out workflows where multiple agents coordinate with each other. This means anticipating failure points in agent communication, designing for moments when the AI misinterprets user intent, and creating conversation flows that feel as natural as traditional interfaces feel visual.
Working alongside AI engineers and data scientists will only become more important as designers need to understand what contextual signals the AI requires to make informed decisions. Instead of only guiding people through layers of screens with buttons, swipes, and menus, the focus will shift to social anticipation and how to best serve up options for desired outcomes.
So, the real measure of success isn’t having the flashiest interface. It’s when you simply state what you want or give the AI enough context that it can predict this, and then the technology orchestrates everything for you. It should be so smooth that you only notice the outcome, not the process. AI will become indispensable not by demanding our attention as a new interface, but by becoming almost invisible and getting us exactly what matters.
