Designing systems for AI agents: What makes a good AX?

Your new digital colleagues are already here—tireless, fast, and frustratingly literal. Are your systems ready to work with them?

Imagine you’ve just hired a new colleague. They don’t need lunch breaks. They work around the clock. They’re fluent in hundreds of systems, follow instructions to the letter, and never complain.

But they also don’t ask questions when something’s unclear. They won’t intuit what you meant. And if your internal tools are confusing or inconsistent, they’ll fall apart—silently, and at scale. That’s why designing systems for AI agents is quickly becoming a new kind of UX challenge—one rooted in logic, structure, and intentionality.

Welcome to the reality of working with AI agents.

These software-based coworkers are already inside your systems: answering support tickets, analyzing data, writing copy, onboarding employees, and summarizing calls. They’re capable, tireless, and growing in number. 

Salesforce CEO Marc Benioff predicts that by the end of fiscal year 2026, over a billion AI agents will be in operation globally. With that scale, designing systems for AI agents isn’t a future concern. It’s a present necessity.

The question no one can afford to ignore is this: Are you designing your systems to work with them—or are you accidentally setting them up to fail?

This is the design problem of the moment. It’s not about crafting beautiful screens or intuitive buttons. It’s about Agent-Based Experience — or AX– the emerging discipline focused on designing systems for AI agents to navigate, understand, and act within effectively.

And if you care about your human users, you should care deeply about your agents. Because when they fail, the human pays the price.

From nice-to-have to mission critical

When websites began offering dark mode, no one had to explain why. It just felt better. The same goes for responsive design, fast-loading pages, or that magical moment when a chatbot actually answers your question.

Good UX works because it disappears. But the agents we now deploy—virtual assistants, internal bots, AI copilots—don’t care about pixel-perfect buttons or clever animations. They care about logic. Clarity. Structure. Predictability.

For them, APIs are the interface. Schema is the signage. Error handling is the tone of voice.

And as agents become more integrated in our workflows, the design bar is rising. AX is no longer optional. It’s a matter of system health, user trust, and operational efficiency.

In short, designing systems for AI agents means thinking less about visual appeal and more about how logic and structure guide machine behavior.

Why designing systems for AI agents is different

Unlike human users, agents don’t “figure things out.” They need well-defined inputs, structured outputs, and predictable workflows. If your system is inconsistent or ambiguous, agents won’t raise a red flag—they’ll fail silently. That’s what makes designing systems for AI agents such a critical shift in product thinking.

The API Is the new homepage

Let’s start with APIs. They’re the doorways agents walk through to get their jobs done. But too often, they’re a mess: inconsistent naming, unpredictable structures, poorly documented endpoints. To an AI agent, this is like walking into an office where every cabinet is mislabeled and half the instructions are missing.

A well-designed API speaks one consistent language. According to best practices outlined by Ambassador Labs and MuleSoft, that means:

• Naming conventions matter: Use camelCase for properties, plural nouns for resources, and standard verbs like GET, POST, and DELETE.
  
• Consistency rules: Parameters, status codes, and URI formats should follow predictable patterns.
  
• Simplicity wins: Each endpoint should do one thing—and do it clearly.
  

REST and GraphQL remain the dominant formats, but the key is clarity over cleverness. The goal is not to impress human developers. It’s to empower software agents to make accurate decisions with minimal guesswork.

That’s the essence of designing systems for AI agents: remove ambiguity, and agents thrive.

Structured data: The agent’s compass

The real world is messy. Your database shouldn’t be.

AI agents rely on structured data—not just to function, but to function well. It’s a cornerstone of designing systems for AI agents that can interpret, respond, and adapt. JSON-LD and schema.org are two essential tools here, especially when agents need to interpret content across systems or the open web.

JSON-LD (JavaScript Object Notation for Linked Data) gives meaning to the data agents consume. Think of it as tagging reality. Schema.org, meanwhile, provides the vocabulary—standardized labels that help define what a product, article, or event is.

Together, they allow agents to move from raw information to understanding. Google, Gemini, and even ChatGPT all use these standards to improve results. If your site or system lacks this kind of metadata, you’re essentially whispering to your AI agents in a noisy room. With it, you’re speaking their native language.

The first day on the job: Agent onboarding

In human teams, onboarding makes or breaks early performance. The same holds true for agents.

Before deployment, agents need clear goals and access to the right data. Think of it as writing a job description:

• What tasks will this agent perform?
  
• What systems does it need to access?
  
• What counts as success?
  

If that sounds simple, consider this: training material must be complete, up-to-date, and free of bias. The data can’t just be accurate—it needs to be structured for machine learning.

One best practice? Start with a pilot. Deploy a limited-scope version of your agent, test real-world usage, and refine based on performance. Then layer in complexity gradually.

Zendesk, for example, emphasizes cross-functional teams during onboarding to ensure each agent deployment meets real organizational needs—not just technical ones.

These steps are fundamental when designing systems for AI agents that can learn and grow over time.

The trust gap: Explainability and transparency

Here’s where AX overlaps with ethics: when an agent makes a call (like if it approves a loan, flags a document, routes a customer) it needs to be able to explain itself.

Explainability is more than a nice-to-have. It’s how we build trust in autonomous systems. And it’s essential when designing systems for AI agents that make decisions impacting real people. If a decision feels random or opaque, users will opt out, escalate, or abandon the experience altogether.

The tools are already here:

• LIME and SHAP provide case-based and feature-driven justifications.
  
• Natural language generators can summarize reasoning in human-readable text.
  
• Visual explanations like saliency maps can illustrate what the agent “saw.”
  

But here’s the twist: different users need different explanations. Developers may want the data trail. End users need simple, clear, and contextual insights. AX designers must account for both.

Error-proof or error-ready?

Even the best-designed agents will fail.

They’ll misinterpret intent. Hit dead ends. Hallucinate facts. Or get tripped up by subtle misconfigurations. The difference between good and bad AX isn’t whether errors happen—it’s how they’re handled.

Effective AX systems include:

• Self-correction loops: Let agents reflect and retry when tasks fail.
  
• Clear feedback channels: Let users flag mistakes easily.
  
• Fallback plans: When agents falter, escalate gracefully—to a human, another agent, or a simpler interaction.
  

Netflix, Tesla, and Klarna all employ agents that improve over time, not just by learning from success but by learning from failure.

Design patterns like the “Reflection Pattern” or “Planning Pattern”—borrowed from agentic architecture—help agents adjust on the fly, making recovery part of the system design.

These recovery loops are key when designing systems for AI agents that are resilient and self-improving.

The unspoken truth: Humans still matter

For all this talk of machine logic, AX is still ultimately about people.

Every system the agent interacts with was built for a purpose—and that purpose usually starts with a human need. So the real art of AX is balancing efficiency for agents with clarity for humans.

That means transparency in agent behavior. The ability to step in when needed. And interfaces that don’t just assume trust—but earn it.

And let’s be honest: even as agents take on more tasks, the humans behind the scenes—designers, engineers, strategists—still set the rules.

AX doesn’t replace human-centered thinking. It extends it.

Designing for the future (that’s already here)

Agent-based experiences aren’t sci-fi anymore. They’re in your inbox, your CRM, your checkout flow. And as adoption grows, design leaders face a choice:

Do you optimize your systems for digital colleagues—or keep pretending they don’t exist?

Because when your systems speak the agent’s language—through clear APIs, structured data, thoughtful onboarding, transparency, and robust recovery—you’re not just future-proofing your tech stack.

You’re designing systems for AI agents that operate seamlessly and invisibly alongside their human counterparts.

And when they don’t, your users will still hold you accountable.