Why This Integration Matters

InterSystems continues to push AI capabilities forward natively in IRIS — vector search, MCP support, and Agentic AI capabilities. That roadmap is important, and there is no intention of stepping back from it.

But the AI landscape is also evolving in a way that makes ecosystem integration increasingly essential. Tools like Dify — an open-source, production-grade LLM orchestration platform — have become a serious part of enterprise AI stacks.

Global Guard AI

1 Introduction

In environments that use InterSystems IRIS, globals are the physical foundation of data storage. Although system queries and administrative tools exist for metric inspection, global growth analysis is usually reactive: the problem is generally only noticed when there is disk pressure or performance impact.

Global Guard AI was developed to create a snapshot-oriented observability layer, aligned with the idea published in DPI-I-512 — and based on the series of articles written by Ariel Glikman, Sales Engineer at InterSystems:

• Part 1 – Data Collection
• httPart 2 –

Hello Community,

We are excited to announce that registration is now open for the second cohort of the course:

 🧑‍💻 Developing FHIR Applications Using Python 🧑‍💻

This hands-on program is designed for developers who want to build real-world FHIR applications using Python and InterSystems IRIS for Health. 

👉 Watch 5-minute course overview

📅 Second cohort starts March 29, 2026

iris-budget

I created iris-budget app for the InterSystems Full Stack Contest in 2026. By full stack, we mean a frontend web or mobile application that inserts, updates, or deletes data in InterSystems IRIS via REST API, Native API, ODBC/JDBC, or Embedded Python.

My app uses multiple REST APIs to add a new category or retrieve a list of categories of expenses and income.

First web application /csp/coffee

I inherited /csp/coffee from module.xml in iris-fullstack-template.

Second web application /csp/budget

For this project, I created a swagger file called "budget.json." I wanted to use the spec

In the modern healthcare landscape, finding clinically similar patients often feels like looking for a needle in a haystack. Traditional keyword searches often fail because medical language is highly nuanced; a search for "Heart Failure" might miss a record containing "Congestive Cardiac Failure."

I am excited to share iris-medmatch, an AI-powered patient matching engine built on InterSystems IRIS for Health. By leveraging Vector Search, this tool understands clinical intent rather than just matching literal strings.
## The Core Innovation: Semantic Clinical Search

`iris-medmatch` bridges the

Hello everyone,
I’m looking to implement Continuous Training (CT) as part of an MLOps strategy for some data science projects in IRIS. I want to automate the full cycle:

- Monitoring model performance & accuracy degradation.
- Retraining models automatically.
- Validating and updating production models.

I’ve looked into IntegratedML, but it seems more focused on the SQL interface for training (AutoML). Even with the new Custom Models (beta), which allows for more flexibility with Python, it doesn't seem to provide the "Continuous" orchestration out of the box.

I’d like to know: