What’s New in io.Intelligence (And What It Means for Getting AI Into Production)

When we first introduced io.Intelligence, the goal wasn’t to launch another AI product. It was to solve a problem we were hearing from clients. Teams had already begun running pilots and testing models, but when it came time to move into production, things stalled.

Questions around how to connect AI to real applications, how to embed it into existing workflows, and how to avoid creating yet another disconnected interface came up every time.

We built io.Intelligence to close that gap by extending the platform teams already have, rather than replacing it. The interop.io MCP-Core connects to existing applications instead of replacing them, and Working Memory feeds context automatically without requiring manual context engineering or being limited by prompt input.

Now we’re expanding io.Intelligence with three new additions, giving teams the flexibility to start fast or customize every detail.

io.Assist

For teams that want to move quickly, io.Assist provides a ready-made assistant experience embedded directly within their environment—no need to build everything from scratch.

Out of the box, io.Assist includes:

• Conversation threads and prompt handling
• Tool execution with traceability
• MCP integration and Working Context support
• A fully usable assistant UI inside your platform

Starting with io.Assist doesn’t limit future flexibility. Because it’s built on top of AI Web and the same underlying architecture, teams can evolve toward more customized experiences over time without starting over.

AI Web

For teams that need more control, AI Web provides a framework-agnostic web SDK for building fully customized assistants and copilots.

It includes all the capabilities of io.Assist—agent conversations, thread management, MCP connectivity, prompt management, MCP Apps support, and optional Working Context integration—while leaving UI, layout, and interaction design entirely in your team’s hands.

AI Server

What ultimately makes either approach viable in production is consistency, visibility, and control.

AI Server provides the backend layer that supports:

• Agent execution and orchestration
• Streaming responses
• Conversation and thread management
• The io.Intelligence Agent Protocol

This creates a clear separation between the user-facing experience and the agent runtime, which is essential for reliability, governance, and long-term maintainability.

What’s Ahead

Our roadmap includes several capabilities that extend the framework further. Code Mode will allow agents to write code that calls tools programmatically instead of requiring each tool to be described individually, reducing context size.

We are also investing in context window management and OTEL-based observability to give teams full visibility as they deploy io.Intelligence in production.

Our direction is driven by real customer needs. If you want to talk through the roadmap or how this fits your specific use case or environment, reach out—we’re happy to discuss.