Agent Experience
The Evolution of Experience Design in the Age of AI
Experience design has evolved from serving humans (UX) to developers (DevEx), and now to AI agents (AX). As billions of agents come online, their experiences will drive relevancy, preference, and adoption for all sites and services. Discover the next frontier of interaction design.
UX
For Humans
DevEx
For Developers
AX
For AI Agents
The Evolution of Experience Design
Understanding how we got here and where we're heading in the age of AI agents.
What Is UX? The Origin of Experience Design
Before we dive into the future, let's understand where we began. User Experience (UX) revolutionized the way we think about interfaces. It transformed software from mere utility into something that resonates with human needs and behaviors.
UX isn't just about making things pretty. It's about empathy, flow, and interaction, designing for how humans feel and behave. It's where psychology meets technology, where art meets function. Great UX turned software from tools into products people love.
DevEx: When Developers Became Users
As software teams grew larger, a new realization emerged: developers themselves are users too. Developer Experience (DevEx) became the discipline of making tools and platforms that developers actually want to use.
DevEx recognized that great tools lead to better software. Clear APIs, excellent documentation, seamless workflows, and minimal friction became the hallmarks of developer-loved platforms. Companies like Stripe, Vercel, and GitHub proved that great DevEx drives adoption.
Enter AX: Designing for AI Agents
Now, as AI agents become more autonomous and capable, a new discipline emerges: Agent Experience (AX). AX refers to the holistic experience AI agents have when interacting with a product, platform, or system. It encompasses how easily agents can access, understand, and operate within digital environments to achieve user-defined goals.
AX is fundamentally different from UX and DevEx. Agents don't have emotions or personal preferences, but they do have capabilities, constraints, and goals. AX focuses on making systems that are agent-readable, agent-operable, and agent-optimizable. It's about crafting product experiences specifically for AI agents, with clean APIs, machine-ready documentation, and workflows that enable seamless agent collaboration.
Understanding the Differences
How UX and DevEx differ in focus, approach, and measurement.
UX Design
DevEx Design
Why AX Matters for Business
Agents are no longer experimental tools, they're becoming integral parts of business operations. The businesses that win with AI won't just use agents. They'll design for them. Too many companies are focusing on adding shallow AI features all over their products or building yet another AI agent. The real breakthrough will be thinking about how your customers' favorite agents can help them derive more value from your product.
Business Without AX
Business With AX
What is Agent Experience (AX)?
Agent Experience (AX) refers to the holistic experience AI agents have when interacting with a product, platform, or system.
Similar to UX (User Experience) and DX (Developer Experience), AX is becoming a key differentiator in software design as AI agents increasingly act autonomously to assist users with digital tasks. AX encompasses how easily agents can access, understand, and operate within digital environments to achieve user-defined goals.
Note: Superagentic AI learned about Agent Experience (AX) from the canonical resource at agentexperience.ax, which defines AX and brings together industry collaborators to pave the path forward for this emerging discipline.
Key Questions for AX
- • Is it simple for an agent to get access to operating a platform?
- • Are there clean, well-described APIs that agents can operate?
- • Are there machine-ready documentation and context for LLMs?
- • Can agents properly use the available platform and SDKs?
AX Principles
- • Agent-readable interfaces and documentation
- • Clean APIs optimized for LLM understanding
- • Seamless human-agent collaboration flows
- • Open approach enabling external agents
Benefits of Designing for AX
When we intentionally design for AX, we unlock significant benefits that compound over time.
Higher Agent Success Rate
Agents complete more tasks successfully with fewer retries and failures
Lower Error & Failure Loops
Reduce costly error-correction cycles and agent confusion
Faster Task Completion
Optimize agent pathways for speed and resource efficiency
Safer & Predictable Automation
Build guardrails for consistent, safe agent behavior
Easier Debugging
Trace and fix agent issues with built-in observability
Better Human-Agent Collaboration
Create seamless handoffs between humans and AI systems
Closed vs Open: Two Approaches to AX
As AI agents become useful and commonplace, we're seeing two broad approaches to enabling agents to interact with software.
Closed Approach
Companies tightly integrate their own agents into their own software. Examples include Google Workspace with Gemini AI buttons and Microsoft Office 365 with Copilot agents.
Open Approach
Companies focus on making their software accessible to external agents. This enables users to bring their favorite agents to help with tasks across different products.
The Open Agent Ecosystem Vision
Leaning into AX as a strategy means embracing a vision of an open agent world. This vision aligns with the original ethos of the open web: a place where many diverse competing agents (built by different people or companies) can seamlessly interact with software on behalf of their users. Prioritizing AX makes it as simple as possible for any agent a user prefers, to deliver outcomes on their behalf.
AX in Practice: Real-World Examples
Companies are already embracing AX as a discipline and recognizing agents as a crucial new persona for their software.
Netlify
Built a Netlify GPT integration that allows ChatGPT to deploy projects to URLs. Today, more than 1,000 sites are created on Netlify directly from ChatGPT every day. This happened because they focused on what agents need from their platform and adapted their API to be optimized for LLMs.
Clerk
Working on the agent experience of their authentication platform. Making it simpler for agents from Bolt, Lovable, or Windsurf to build applications handling authentication. Also tackling making it easy for agents to sign in to applications built with Clerk.
Neon
Deeply invested in making sure Neon caters to agents. Already staffed a team of AI engineers to dogfood Neon as agentic infrastructure, positioning it as the default Postgres provider of choice for agent-powered applications.
The Future Is Agent-Native
Designing for agents isn't a nice-to-have, it's quickly becoming a necessity. Agent-Native design is the next infrastructure revolution, and AX is its frontend.
Agent-Ready APIs
Agent Sandboxing
Agent Observability
Agent Guardrails
Agent Memory
Agent Orchestration
Agent Feedback Loops
Human-Agent Interfaces
Ready for the Agent-Native Future?
The organizations that master AX will have a significant competitive advantage in the AI-driven future. Generic SaaS tools will increasingly be replaced by custom developed internal applications. Entirely new paradigms of web experiences will emerge as developers can build things of a complexity previously unimaginable.
Agents will far more frequently be collaborators and extensions of humans, rather than replacements. Both will wildly increase the productivity and the ability of a single human being. For all software companies, this shift demands a fundamental change in mindset: start consciously designing the AX of their products, or risk being replaced by tools that empower their customers to harness the exponential power of seamlessly collaborating with agents.
SpecMem: Exploring Agent Experience for Coding Agents
SpecMem represents Superagentic AI's first attempt to explore and implement Agent Experience principles specifically designed for coding agents.
SpecMem: Agent Experience Layer
SpecMem is our Agent Experience (AX) memory layer designed to optimize how coding agents interact with codebases, understand context, and maintain state across development sessions. It's built with AX principles in mind, focusing on making codebases agent-readable, agent-operable, and agent-optimizable.
Agent-Readable
Structured code representations that agents can easily parse and understand
Agent-Operable
Clean APIs and interfaces that enable seamless agent interactions with codebases