Our AI orchestration services
MCP server development
The Model Context Protocol is becoming a key standard for communication between AI agents and external systems. We design and build MCP servers that make your services, APIs, and data sources accessible to AI agents, without each agent requiring custom integration.
CLI integration for agent orchestration
Not every system has an API layer, and not every system needs one. CLI integration gives AI agents direct access to tools, build pipelines, legacy systems, and infrastructure via the command line. We advise on which approach fits your architecture: MCP for standardised service interfaces, CLI for direct system access.
Multi-agent coordination
Complex tasks often require agents that work together; an orchestrator distributing subtasks across specialised sub-agents, aggregating results, and monitoring execution. We design multi-agent architectures that are scalable and traceable, with clear responsibility boundaries per agent.
A2A protocol integration
The Agent-to-Agent protocol enables agents from different systems and vendors to communicate without a central coordinator. We implement A2A as an extension to MCP orchestration for complex multi-agent environments where multiple autonomous agents collaborate.
Business system integration via agents
CRM, ERP, CMS, and BI platforms are often the systems your AI agents need to consult and control. We build the orchestration layer that makes those connections secure, controlled, traceable and reliable.
Agentic workflow design
Effective agentic workflows start with the work process, not the technology. Which steps should the agent execute autonomously? Where is human validation needed? Where are the security boundaries? We design those workflows before writing a single line of code.
Let's discuss your AI orchestration needs
Our approach to AI orchestration
Connecting an agent securely, observably, and at scale to your CRM, ERP, and CMS is a different discipline from building an agent that only generates text. Our approach centres on three principles: the right protocol for the right context, security built into the design, and architectural blueprints that make the transition from POC to secure production more reliable.
The right protocol for the right context
MCP is the right choice when exposing existing services and APIs to AI agents via a standard protocol. CLI integration fits when no API layer exists or when direct access to tools, build pipelines or infrastructure is preferred. A2A suits multi-agent setups where systems collaborate autonomously. Spring MCP aligns with Java/Spring Boot. We choose protocols based on technical context and business requirements, not implementation frequency.
Security as an architectural component
An agent that initiates a payment, places an order, or modifies operational data has different security requirements from one that only retrieves information. Intent verification, transaction limits, audit trails, and error handling need to be built in at the protocol level. In the Worldline implementation, security and compliance at the agent-payment boundary were core architectural considerations from the start.
From POC to production
Most MCP implementations start as proof-of-concept and remain there. The gap between a working demo and a production system is architectural rather than technical: what security requirements apply, how do you scale to multiple agents, how do you keep workflows traceable? The Brussels Airport POC delivered an architectural blueprint that addresses those questions. The Worldline implementation demonstrates how to make that transition to production.
Why work with iO?
Production MCP, not just demos
We have MCP implementations in production at Worldline (payment services) and Etex (ContentHub integration). Worldline is one of the first major global payment providers with production MCP servers: a distinction that separates us from vendors who write about MCP but haven't deployed it in production.
Protocol breadth from hands-on experience
We have built production or proof-of-concept experience across MCP, CLI integration, A2A, Spring MCP, and WebMCP. That makes our recommendations concrete: we know which framework fits a Java/Spring Boot environment, which suits a payment provider on Google Cloud infrastructure, and which fits an agentic multi-brand platform.
Integration depth from systems integration experience
AI agents are only as capable as the systems they're connected to. We have worked as a systems integrator for organisations like Worldline, Brussels Airport, and Etex for years, giving us a thorough understanding of their business system architectures. That depth is why our MCP implementations go beyond demo APIs.
From orchestration architecture to business outcome
An AI agent that processes payments without human intervention is technically interesting. The business case, however, is what matters: lower operational costs, faster transaction processing, and a payments infrastructure ready for the agentic commerce era. We help you make that translation, for your board and your CFO.
