Your ecosystem, ready for agents
Your customers increasingly use AI agents to handle their purchases, bookings and service enquiries. If your systems aren't agent-ready, you lose those interactions. Customer journeys are no longer exclusively human. AI agents now navigate, compare and act, alongside and on behalf of people. Being agent-ready goes beyond MCP. It means your content is discoverable for AI via llms.txt, your ecosystem is accessible via CLI interfaces alongside MCP endpoints, and your platform is usable for AI agents that navigate your interface visually via computer use, just as a human would. We ensure your digital environment is discoverable and usable at all those layers. Agent Experiences is about the infrastructure that makes your ecosystem agent-ready: structured data, MCP endpoints, CLI interfaces, llms.txt and computer use-ready design. Not about what agents do (that's Agentic Commerce), but about how your systems can find and use agents.
From static endpoints to agent-usable interfaces, without throwing your existing stack overboard
MCP, CLI, llms.txt and computer use as concrete building blocks for a fully agent-ready ecosystem
Proven in production: Brussels Airport, Worldline and Feyenoord already made the leap
Designed for both human visitors and the AI agents increasingly acting on their behalf
Get started quickly with a targeted agent-readiness scan of your current digital ecosystem
MCP server development
We build MCP (Model Context Protocol) servers that make your existing systems accessible to AI agents. This enables agents not only to retrieve information, but also to genuinely take action, from looking up flight information to initiating payments.
CLI integration alongside MCP
MCP and CLI aren't competitors, they complement each other. MCP is ideal for structured API communication with multi-tenant authentication. CLI provides agents with direct system access via interfaces that LLMs have been trained on for years, resulting in higher reliability and lower cost per operation. iO pursues both paths and helps you determine which approach fits each integration challenge in your ecosystem.
llms.txt and AI discoverability
How discoverable is your content for AI? An llms.txt file gives LLMs a structured, human-curated map of your key content, so agents understand your services, products and documentation correctly without relying on outdated training data. We implement llms.txt as part of a broader AI discoverability approach, alongside structured data and semantic markup.
Computer use-ready platform design
AI agents like OpenAI Operator and Anthropic Computer Use navigate your web interface visually, just as a human does. They click, scroll, read and fill in forms. If your platform isn't built for that, those interactions fail. We assess and strengthen your digital environment so it's usable for both human visitors and AI agents operating via computer use.
Agent-ready ecosystem design
Assess your digital architecture for agent readability and implement the adjustments that make the difference. We design ecosystems that are intuitive to use for both human users and incoming AI agents, whether they arrive via MCP, CLI, browser-level computer use or structured data.
Structured data for AI agents
AI agents reason on the basis of structured, machine-readable data. We translate your existing content, product data and platform information into formats that agents can directly understand and use, no human interpretation needed.
WebMCP integration
WebMCP brings MCP functionality to the browser and makes your web platform directly accessible to AI agents via an open standard. We implement WebMCP so your digital environment is ready for the next generation of agent-driven traffic.
Multi-step agentic workflows
One question, multiple actions. We design and build agentic workflows where an AI agent takes multiple steps in sequence, from intent to outcome, by intelligently orchestrating tools, data sources and systems.
Our approach to agent-ready ecosystems
The customer journey used to mean a human navigating your website. Increasingly, it means an AI agent arrives on behalf of a human, via MCP, via a CLI interface, via llms.txt, or by visually navigating your interface via computer use. Agent Experiences isn't about building AI agents. It's about making your existing digital environment accessible to agents arriving from outside. We begin with concrete analysis of your current architecture and build step by step towards an ecosystem that agents can find, understand and use.
From informational to actionable
Traditional digital platforms provide information back to people. Agent-ready platforms go further: they enable AI agents to take action, initiate payments, request data, start processes. The difference lies in architecture, not intent. We design that distinction deliberately. Agent Experiences is the infrastructure layer: MCP servers, CLI interfaces, structured data, WebMCP endpoints. Agentic Commerce is the transaction layer, agents that purchase, book, pay. That separation determines who's at the table: here it's your platform and data architecture team.
Discoverable for every agent, at every layer
Agent readability starts with structured data, but extends through to architecture level. llms.txt ensures that LLMs understand your content via a structured, human-curated entry point. WebMCP makes your web platform usable for browser-level agents. MCP servers give deeper systems a standard interface. CLI integrations provide a direct, reliable path for agents that need local or system access. Computer use-ready design ensures AI agents can navigate your platform visually, just like a human user. We address all these layers.
Proven in practice, scalable to production
We work with open protocols. MCP is an open standard, and we've already proven it in practice at Brussels Airport, Worldline and Feyenoord. From proof of concept to production is a deliberate step we guide carefully, with observability and quality assurance built in from the start.
Why work with iO?
MCP expertise built in production.
We didn't discover MCP from a blog post, we built it for Worldline (live, January 2026), Brussels Airport (proof of concept) and Feyenoord (DXP integration). That hands-on experience makes the difference when you're ready to move to production. The difference between 'AI-ready' and 'agent-ready' is fundamental. AI-ready means you can call an LLM. Agent-ready means external agents can discover, understand and use your services, without human intervention. iO builds that layer.
We choose the right path for each integration challenge.
MCP and CLI are complementary approaches. MCP is the choice for structured API communication, multi-tenant security and enterprise OAuth requirements. CLI provides direct system access, higher reliability and lower costs for integration points where LLMs have already been trained. We're not dogmatic: we choose per situation the path that best fits your ecosystem, your team and your objectives.
We make your existing ecosystem agent-ready.
Agent Experiences doesn't require complete rebuilding. We assess your current digital environment, CMS, APIs, data structures, CLI interfaces, and determine the targeted adjustments that open your platforms to AI agents. Quick results, without unnecessary disruption.
Technology-agnostic, standards-driven.
MCP is an open standard, and that's precisely why we use it. We choose protocols that fit your stack and scale, no vendor lock-in, no closed ecosystems that make you dependent on one supplier.
From architecture to business case.
An agent-ready ecosystem is a technical question and a strategic choice. We help you build the internal business case: what does it deliver, which customer journeys improve, how do you position this to your leadership? So technology and business goals align.
Platforms and technology we deploy
We choose the platform that fits your context, not the platform we happen to know best. Our AI Engineers work with tools that perform reliably in production.
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MCP / Spring AI
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