First we had generative AI—tools like ChatGPT that assist when prompted. Now we’re entering the age of agentic AI: systems that not only respond but also act, plan, and adapt on their own.
This shift from assistant to agent is more than a technical upgrade. It changes how we design user experiences, structure content, and build workflows. And it’s happening fast.
What Is Agentic AI?
Unlike traditional generative AI, which needs specific input to produce output, agentic AI systems operate autonomously. They can:
- Set goals
- Make decisions based on feedback
- Perform multi-step tasks over time
- Adapt to changing conditions without ongoing user input
Think of it this way: a generative tool writes your blog draft after being prompted to do so. An agentic tool writes, edits, formats, posts, and promotes it—autonomously, on repeat.
According to TechRadar, agentic AI will “redefine the future of work by taking on complex, multi-layered responsibilities that traditionally required human coordination.” This includes customer support, marketing campaigns, UX optimization, and more.
Wait—Is Agentic AI the Same as an LLM?
Not quite. While they’re closely connected, agentic AI and large language models (LLMs) are not the same thing.
LLMs (like ChatGPT, Claude, and Gemini) are trained to generate and understand language.
They’re great at answering questions, summarizing content, or writing copy, but they’re fundamentally reactive. They wait for a prompt before responding.
Agentic AI, on the other hand, refers to AI systems that can act autonomously.
They don’t just respond—they act. Agentic AI sets goals, makes decisions based on context and feedback, executes multi-step tasks, adapts in real time, and works continuously over extended periods, making it the most involved model yet.
Agentic AI systems often use LLMs as one component, but they also include memory, task planning, action-taking logic, and often external tools (APIs, web browsing, scheduling, etc.).
Think of it this way:
LLM = The brain
Agentic AI = The full system with brain, body, and autonomy
If an LLM helps a user draft an email, an agentic AI could draft, personalize, schedule, and send it—then follow up based on replies.
Why It Matters for UX and Digital Strategy
1. Less Clicking, More Doing
Agentic AI shortens the distance between intent and action. For example, instead of clicking through a multi-step onboarding process, a user might simply state a goal—“I want to grow my email list”—and the agent configures the right tools, integrations, and CTAs automatically.
This creates a shift from interface-based design to intent-based design. Interfaces still matter, but they need to:
- Surface just enough context
- React fluidly to user feedback
- Help users understand what the AI is doing on their behalf
2. Designing for Trust and Transparency
As AI takes on more initiative, users need clear guardrails and transparent systems. What is the AI allowed to do? How does it make decisions? Can the user intervene?
These are UX challenges, not just technical ones. UX patterns around confirmations, progress states, and fallback options will become even more critical.
3. AI as a User (Not Just a Tool)
In some workflows, AI will act as a user of your product—navigating your site, pulling data from your CMS, or interacting with support tools. This flips UX on its head: Are your systems readable and accessible not just to humans, but to machines working on human behalf?
Designers may need to collaborate more closely with AI developers to ensure that interfaces are optimized for both human and non-human interaction.
Agentic AI in Action
According to Vogue Business, brands are already experimenting with agentic AI for tasks like:
- Virtual styling and customer service in fashion retail
- Real-time campaign iteration in marketing
- Automated personalization based on observed behavior
Adobe has launched a new line of AI “agents” to handle campaign setup, asset creation, and even A/B testing across platforms like Microsoft Copilot.
These aren’t just AI assistants—they’re autonomous actors embedded into the user journey.
Google’s new AI Mode in Shopping uses Gemini to deliver a more agentic experience—building outfits, adapting to feedback, and refining suggestions in real time. Instead of just reacting to single queries, it works toward user-defined goals like “find a travel outfit,” blending virtual try-on, conversational filtering, and continuous task execution—hallmarks of agentic AI.
UX Anti-Patterns to Avoid in the Age of Agentic AI
As powerful as agentic AI is, it also introduces a new layer of risk in how we design user experiences. When systems start making decisions on behalf of users, clarity, feedback, and control become more important than ever.
But not all design choices scale well in this new AI-driven context.
In UX, an anti-pattern is a design choice that seems helpful on the surface but ends up causing confusion, frustration, or unintended consequences. And agentic systems, because they act without constant user input, make it easier to accidentally fall into these traps.
Here are a few emerging anti-patterns to watch out for:
1. Invisible AI
The AI performs actions without informing the user—like auto-saving drafts, sending messages, or triggering events silently. This erodes trust and leaves users feeling out of control.
Solution: Always surface what the system is doing behind the scenes. Consider progress indicators, passive notifications, or “AI activity logs.”
2. Over-Persuasion
AI tends to nudge users toward a specific path without enough context. Think of an onboarding flow that assumes your goal—or worse, completes it for you.
Solution: Use adaptive suggestions, not assumptions. Let users affirm or redirect the AI’s logic before major actions.
3. Interruptive Validation
The AI asks the user to confirm every minor decision it makes, defeating the purpose of automation and adding friction to simple flows.
Solution: Reserve confirmation prompts for high-risk or irreversible actions. For everything else, show what happened and allow undo.
4. Unclear Authority
Users don’t know who (or what) is responsible for a given decision—the app, the AI, or an integration. This creates confusion when things go wrong.
Solution: Make system roles transparent. Is the AI suggesting or executing? Is this change final or editable?
Just like we design for accessibility, privacy, and clarity today, we’ll need a new layer of UX standards for agentic systems. Treat the AI as a co-pilot—not a ghost in the machine—and users will follow your lead.
What This Means for Teams
Agentic AI doesn’t eliminate design, strategy, or content. It demands a new kind of collaboration—between humans and tools that can think (and act) for themselves.
This creates a number of opportunities including:
- Streamlining UX flows for goal-driven automation
- Building in checkpoints where users can supervise or adjust AI decisions
- Designing adaptive UI layers that update in real-time
- Treating AI not just as a backend layer, but a co-pilot in the interface
At Composite, we believe the best user experiences are the ones that anticipate needs without sacrificing clarity. Agentic AI just raised the bar for what that looks like.
Want to future-proof your UX for the age of AI? Let’s talk.
