Agent Experience (AX): The Concept of AI Agent Designing

We are on the brink of a big digital change- one where AI agents take center stage. These autonomous systems are designed to handle complex tasks independently, reducing the need for constant human input.

Picture this: You are preparing for a big project at work. Instead of juggling multiple tools and tasks, you simply provide a few instructions to an AI agent, and it takes over. It schedules meetings, organizes documents, communicates with your team, and even tracks progress, all without your constant intervention. 

This is the essence of Agent Experience (AX): a new way of interacting with technology that emphasizes autonomy, personalization, and efficiency. With the continuous transformation of AI, AX is transforming how we complete tasks and engage with the digital world, offering a seamless experience where agents do the heavy lifting, and you enjoy the results. Let’s learn more!

AX is Taking Center Stage: A New Era Beyond UX and DX

The way we interact with technology has changed over time, from User Experience (UX) to Developer Experience (DX). Now, as AI continues to grow, we are entering a new phase- Agent Experience. 

In a recent post, Introducing AX: Why Agent Experience Matters, Netlify’s CEO Mathias Biilmann explains why AX is the next big thing, just as UX and DX once were.

To understand AX, let’s first look at the impact of UX and DX:

• UX, introduced by Don Norman in 1993, changed the way we think about human interaction with products. It focused on everything from design to ease of use, ensuring that products “felt” right for users.
• DX, popularized in 2011, acknowledged that developers needed better tools, such as clean APIs and comprehensive documentation. This shift led to faster growth and adoption of platforms that made developers’ lives easier.

Now, AI is stepping in with agents powered by large language models and capable of performing tasks on their own. Biilmann argues that it’s time to focus on Agent Experience- the experience AI agents have when using products or platforms. The future of AI isn’t about adding superficial AI features; it’s about designing experiences that allow agents to truly shine to deliver value to users in the process. 

To make this concept more concrete, let’s take a closer look at the difference between poor and great AX:

Poor AX & Great AX

• Poor AX: Imagine trying to get a task completed by an AI agent, but the agent constantly struggles. It’s like giving instructions to someone who doesn’t understand the task, thus leading them down one failed path after another. Each attempt consumes unnecessary resources, and the agent’s responses are sluggish and repetitive. You end up wasting time and energy, ultimately needing to take over the task entirely because the agent couldn’t get it right.
• Great AX: Now, picture the opposite scenario. The AI agent takes on the task with clarity and accuracy to complete every step effortlessly without needing to ask for additional input. The system moves efficiently, using just the right amount of resources, and the task is finished swiftly and accurately. No need for intervention; it’s all handled autonomously, and the process feels almost seamless. The system works so well that it makes you excited to use it again.

Hence, it’s clear that the focus will shift to optimizing AX to ensure that AI agents can deliver high-quality and efficient experiences with minimal user involvement. The future of AI, much like the shifts seen with UX and DX, will be defined by how well we design experiences that allow agents to truly shine.

Let’s explore why AX matters and how it’s set to transform the future of technology!

Why is Agent Experience Important?

Agent Experience is becoming a critical factor as AI agents play an increasingly important role in how we interact with digital products and platforms. As autonomous AI is advancing, focusing on AX is essential for ensuring that these agents are effective, efficient, and ultimately valuable to users.

Here’s why it matters.

1. Efficiency & Cost-Effectiveness: Saving Resources & Money

A great AX allows AI agents to perform tasks efficiently in a single attempt, saving resources and time. In contrast, poor AX leads to agents taking multiple tries, wasting resources, and requiring manual intervention, thus resulting in increased costs and frustration.

2. User Trust and Adoption: Earning Reliability

When AI agents deliver consistent and accurate results, users develop trust in the system, thus providing greater adoption. Good AX means less friction, and users are more likely to rely on agents for their tasks.

3. Competitive Advantage: Staying Ahead

As the use of AI agents grows, platforms that provide poor AX will quickly fall behind. To stay ahead, businesses must offer agent-friendly ecosystems featuring easy onboarding, accessible APIs, and secure authentication. This makes the platform more adaptable and attractive to users.

4. Scalability & Future-Proofing: Growing Smoothly

AX prepares your product for the future, where AI agents may become the primary way users interact with systems. Investing in AX now ensures that products can scale smoothly as AI capabilities advance.

5. Smooth Interactions: Easing Accessibility

AX focuses on how easily AI agents can access, understand, and operate within platforms to complete user-defined tasks. A seamless AX enables agents to work autonomously without needing constant human input or troubleshooting.

In short, a well-crafted AX enables smoother operations, builds user trust, and prepares businesses for the next era of AI-driven interactions. Let’s further discuss two approaches to embracing agent experience.

Closed & Open Approach

As AI agents become more integral to our digital lives, the way they interact with software will evolve into two main approaches: closed and open. These approaches will shape the future of Agent Experience and determine how users and agents can collaborate with platforms.

The Closed Approach

In a closed model, companies tightly control the AI agents that work within their software ecosystems. For example, products like Google Workspace and Microsoft Office 365 are integrating their proprietary AI agents, such as Gemini and Copilot, into their platforms.

This makes using the tools easier, but it also limits flexibility because users can’t bring outside agents to interact with the software. Although this is a common approach, it can slow down innovation and reduce the freedom of agents, leading to a disconnected experience.

The Open Approach

On the other hand, an open approach is centered around flexibility and collaboration. Companies adopt an open strategy to allow external agents to interact with their platforms seamlessly.

This creates a rich ecosystem where users can choose the best agent for the task at hand, providing more diverse, powerful, and personalized experiences. In this model, platforms focus on providing open APIs, accessible documentation, and user-friendly onboarding for agents, hence making it easier for them to collaborate across various tools.

The key to a better Agent Experience lies in embracing the open approach. By allowing users to bring their preferred AI agents into any platform, companies can create an environment of innovation. This is where agents from different sources collaborate freely to achieve better outcomes. This open ecosystem is similar to the spirit of the open web, where different and competing technologies collaborate to meet the needs of users.

Also, in contrast to the restrictive nature of the closed approach, an open model encourages agility, scalability, and adaptability. Ultimately, embracing an open agent world not only benefits users by giving them control but also gives a more dynamic and collaborative environment for developers and businesses.

Dive deeper:  ‘How to build an AI agent’!

Further, designing the right Agent Experience will be important for getting the full potential and enabling agents to work seamlessly across various platforms. Let’s check some important considerations!

How to Build a Great Agent Experience

Creating a standout Agent Experience requires a shift in how we think about user and developer interactions. While many of the principles that made UX and DX successful still apply, AX has its own unique challenges due to the way AI operates. 

Here are some key principles for building a great AX:

1. Optimized Communication for AI

Unlike humans, who are naturally adept at interpreting visuals and icons, AI agents thrive on data that’s structured and easily digestible. Since AI’s native language is numerical embeddings, it’s crucial to design AX around methods that are optimized for AI’s consumption. 

This focuses on clean data structures and minimizing unnecessary computational overhead. This further enables smoother and faster interactions between agents and platforms.

2. Clear and Accessible APIs

A clean and well-defined API is foundational to any great Agent experience. Just as good DX focuses on easy-to-use APIs for developers, AX requires APIs that agents can reliably interact with. 

These APIs should offer clear documentation, stable endpoints, and well-structured data. Many products that didn’t require APIs before will need to invest in building or enhancing them to ensure agents can access complete functionality.

3. Simple Onboarding for Both Humans and Agents

To make the interaction seamless, onboarding should be as frictionless as possible for both the user and the agent. Providing easy and intuitive setup processes, such as one-click commands, ensures that agents can start working quickly without overwhelming the user. 

The smoother the initial experience, the more likely users will adopt and trust the agent to perform tasks.

4. Efficient & Automated Agent Operations

Once the agent is onboarded, the next is to make sure its operations are as frictionless as possible. Agents should be able to complete tasks without unnecessary manual steps. Whether it’s initiating actions, managing resources, or analyzing data, the process should be automated and efficient. This reduces the workload for both users and the agents themselves, ensuring productivity is maximized.

5. Human Oversight When Necessary

While agents should be able to operate autonomously in many cases, there will be situations where human input is essential. For high-stakes actions, like financial transactions or changes to critical systems, an approval workflow that requires human verification should be in place. This ensures a balance of autonomy and control to increase trust in the agent’s capabilities.

By focusing on clean APIs, simple onboarding, and frictionless operations, you not only create a better AI agent experience but also enhance overall user satisfaction. These thoughtful design choices improve the overall experience, thus encouraging higher adoption rates and ensuring that both agents and humans can work together efficiently.

The result?

Agent Experience that’s intuitive and effective ultimately delivers the best result to the users.

Conclusion: Focus on Agent Experience or Lag Behind

As we enter the era of AI-driven interactions, Agent Experience is becoming just as essential as UX and DX once were. Companies that embrace AX today, like Markovate, are positioning themselves at the forefront of this transformation by building platforms that empower agents to operate seamlessly, securely, and autonomously.

The shift towards agent-friendly systems will create more efficient, flexible, and innovative solutions to enable users to collaborate effortlessly with AI. 

As we continue to integrate AI into every aspect of our digital lives, prioritizing AX will ensure businesses stay competitive and future-ready. 

The time to focus on AX is now- it’s the key to unlocking a new wave of possibilities in software design. 

Contact us for more information, and start building your agent-powered future today!

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