Unified AXO Strategy
Core principles and architecture for optimizing digital experiences across websites, services, and applications in an AI-native world.
The Shift to Agent Experience
For decades, we've designed digital systems around two core paradigms: User Experience (UX) for direct human interaction, and Developer Experience (DX) for building and maintaining those systems. AI agents introduce a third dimension—Agent Experience (AX)—where users delegate tasks to intelligent intermediaries that navigate services on their behalf.
This isn't about replacing UX or DX. It's about extending them to support a new form of interaction where the "user" making decisions might be an AI agent acting with human authority. The agent becomes a medium through which people accomplish goals without directly manipulating every interface or API themselves.
User Experience
People directly interact with interfaces—clicking, typing, navigating. Design focuses on clarity, usability, and intuitive workflows.
Developer Experience
Engineers build and integrate systems using APIs, SDKs, and documentation. The focus is on clear contracts, reliability, and efficiency.
Agent Experience
Agents act on user behalf, making decisions across services. Design centers on delegation, context preservation, and mutual value exchange.
How Agents Interact with Your Systems
Agent interactions follow a delegation model, not a bot model. A bot blindly scrapes or executes predefined scripts. An agent receives intent from a user, evaluates options across multiple services, takes action, and returns results—all while learning which services deliver the best outcomes for specific types of requests.
This creates a mutual value exchange: your service gets agent traffic only if it consistently delivers good results. The agent builds trust and preference based on success rates, quality, and reliability. This cycle—delegation, action, delivery, feedback—means services need to prove value to earn repeated agent interactions.
Delegation
A user hands off a task to an agent with context about what they want accomplished. The agent needs enough information to act with appropriate authority and scope.
Action
The agent evaluates options, chooses services based on past performance, and executes the task. Services need to provide clear interfaces and reliable responses.
Delivery & Learning
Results flow back to the user. Success builds agent–service affinity. Failures degrade preference. This feedback loop shapes future agent decisions.
Six Foundational Principles
Whether you're optimizing a website for content discovery, building APIs for service delegation, or designing interfaces for collaborative agent workflows, these principles apply universally. They form the backbone of any effective Agent Experience Optimization strategy.
Human-Centricity
Agents exist to serve human intent, not to replace human judgment. Every agent action should trace back to a person's goal. Systems must preserve user agency even when delegating tasks.
Accessibility
Agents deserve the same access parity that humans enjoy. If a person can do something through your interface, an agent acting with proper authority should be able to do it too—without special accommodations or loopholes.
Contextual Alignment
Agents need operational context—what your service does, how it works, what data formats it expects. Structured metadata, clear documentation, and semantic markup help agents make informed decisions.
Permission Awareness
Agents must respect scoped, identity-based access controls. They should present credentials, honor rate limits, and operate within the boundaries users establish. Security can't be an afterthought.
Observability
Track agent interactions separately from human traffic. Measure success differently. Monitor patterns that reveal how agents evaluate and choose services. Use this data to improve both performance and agent affinity.
Provenance
Maintain clear records of where data comes from, who authorized an action, and how a result was generated. Trust requires traceability. Provenance helps resolve disputes and ensures accountability.
Multi-Agent Ecosystems
The future isn't one agent serving one user. It's humans working with multiple agents, agents collaborating with other agents, and services providing context to any participant that needs it. This creates a human + agent + service triad where shared metadata and protocols enable seamless coordination.
Standards like llms.txt, Model Context Protocol (MCP), and agents.json help establish this shared layer. They provide lightweight, machine-readable formats that describe capabilities, intentions, and operational boundaries—so agents don't have to reverse-engineer your site or API every time they interact with it.
llms.txt
A simple, human-readable file that describes your service's purpose, key pages, and how agents should interact with your content.
Model Context Protocol
An interoperability standard that allows agents and services to exchange context in structured, predictable formats across platforms.
agents.json
A capability manifest that signals what your service offers, how to authenticate, and what actions agents can perform.
The AXO Maturity Ladder
Progressing your Agent Experience Optimization happens in stages. You don't need to reach the highest level immediately. Start where it makes sense for your service, then iterate based on agent interaction patterns and business value.
Level 1: Indexable
Your content is discoverable by agents. You've optimized for crawling, added structured data where relevant, and removed unnecessary barriers. Agents can find you and parse your information without extraordinary effort.
Level 2: Contextual
You provide rich context about your service—what it does, how it works, what data it offers. Metadata layers, clear documentation, and semantic markup help agents understand intent and make informed choices about when to use you.
Level 3: Interactive
Agents can act through your service—submitting forms, performing transactions, receiving feedback. You've built APIs or interfaces that support delegation, authentication, and result delivery in structured formats.
Level 4: Adaptive
Your service learns from agent interactions over time. You personalize responses based on past success patterns, adjust recommendations dynamically, and build mutual trust through continuous performance feedback.
Apply These Principles
Ready to implement AXO? Choose your focus area based on your business model and agent interaction patterns.
Website Optimization
Make your content discoverable and referenceable by AI agents. Structured data, semantic HTML, and agent-readable formats.
Service & App Optimization
Build APIs and services that agents can interact with directly. Enable secure delegation with clear permissions and value exchange.
Agentic Experience Design
Design interfaces for human-agent collaboration. Create transparent, explainable experiences that preserve user agency.
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