#Artificial Intelligence (AI)

In last post I talked about iris-copilot, an apparent vision that in near future any human language is a programming language for any machines, systems or products. Its agent runners were actually using such so-called 3rd generation of agents. I want to keep/share a detailed note on what it is, for my own convenience as well. It was mentioned a lot times in recent conversations that I was in, so probably worth a note.

Hey Community!

We're happy to share the next video in the "Code to Care" series on our InterSystems Developers YouTube:

⏯ MCP Explained: Bridging the Gap between AI Agents and Tools

#North American Demo Showcase entry. 

>> Answer the question below to be entered in the raffle!

⏯️ AI-Assisted Rare and Complex Disease Detection

This demo shows how InterSystems Health Gateway can be used to pull in outside patient records from networks like Carequality, CommonWell, and eHealth Exchange, creating a more complete longitudinal view in a clinical viewer. That full record is then analyzed by AI to surface potential rare disease considerations with clear reasoning, helping clinicians see patterns they might otherwise miss.

Presenters: 
🗣 @Jesse Reffsin, Senior Sales Engineer at InterSystems
🗣 @Georgia Gans, Sales Engineer at InterSystems
🗣 @Annie Tong, Sales Engineer at InterSystems

I'm starting to play more with AI enabled coding. 
I've been using Github Copilot inside Visual studio code, which is very good at coming up with autocomplete suggestions that are accurate and useful. (Along with some utter rubbish, naturally).
For web development I'm starting to use Claude Code in VS Code to help create web sites and integrations. I want to see how it can help with IRIS development. 

However I can't get claude to read any iris code directly as I'm connected to my server via isfs server connections.

#North American Demo Showcase entry. 

>> Answer the question below to be entered in the raffle!

⏯️ AI Assistants for the Unified Care Record Powered by Gemini

In this demo, you will see how Gemini works directly with FHIR data, and how it leverages the harmonized dataset provided by InterSystems Unified Care Record. It also showcases multiple AI assistants helping multiple groups of users, e.g. clinicians, patients.

🗣 Presenter: @Simon Sha, Sales Architect at InterSystems

Hi, Community!

Here's another way AI can speed up your processes—take a look! 👇

Generating Transformation Descriptions with the DTL Explainer

We didn't start with a big AI strategy.

We had a legacy InterSystems Caché 2018 application, a lot of old business logic, and a practical need: build a new UI and improve code that had been running for years. At first, I thought an AI coding agent would help only with a small part of the work. Maybe some boilerplate, some REST work around the system, and a bit of help reading old ObjectScript.

In practice, it became much more than that.

Over the past year there have been a few DC articles offering MCP servers designed to connect to IRIS and help the AI features of VS Code and its cousins do a better job. For example:

• Hey chat, what's up with my Interoperability by @Dmitry Maslennikov 
• No-Hallucinate MCP: Real-time Routine Context for ObjectScript by @Jinyao 
• iris-mcp-atelier introduced by @Ernest Salas in his comment on the Claude Code vs ISFS. Has anyone cracked how to get one to see the other? post by @MarkCharlton.

Motivation

Why do we need this?

1. Lack of Compiled Context: AI tools only see source code; they don't know what the final compiled routine looks like.

2. Macro Hallucination: Because AI doesn't see our #include files or system macros, it often makes them up, wasting time during debugging.

3. The Documentation Gap: Deep logic optimization often requires understanding internal macros that aren't fully covered in public documentation.

4. Manual Overhead: Currently, the only way to fix this is to manually use the IRIS VS Code extension to find the "truth" in the routine.

Hey Community!

We're happy to share the next video in the "Code to Care" series on our InterSystems Developers YouTube:

⏯ Vibe Coding: Letting AI Build While You Steer

A Continuous Training (CT) pipeline formalises a Machine Learning (ML) model developed through data science experimentation, using the data available at a given point in time. It prepares the model for deployment while enabling autonomous updates as new data becomes available, along with robust performance monitoring, logging, and model registry capabilities for auditing purposes.

InterSystems IRIS already provides nearly all the components required to support such a pipeline. However, one key element is missing: a standardised tool for model registry.

Hi folks,

One of the things I've been thinking about recently: how strong AI is changing my work as an ObjectScript developer right now! Yeah, yeah, I know I'm not a very fast, it should have happened 2-3 years ago... But it's only now that I've realized that I am not writing the code all my workday like before.

v2026.1 was just released as GA, and one of the features I'm looking forward to using is the DTL Explainer feature.

This allows you to take a Data Transformation, and with a click of a button get a human-readable description of the transformation (which you can also use as the basis for the DTL Description).

For complex DTLs, especially ones you didn't write yourself, or you did but a long time ago, this will allow you to get a clear quick understanding of what it's doing.