#Embedded Python

A Better Developer Experience With PollingBusinessService

One of the most visible additions in 4.0.0 is PollingBusinessService.

Before this release, creating a scheduled Python business service meant dealing directly with the low-level inbound adapter pattern. In 4.0.0, a scheduled Python entry point can be written directly as a PollingBusinessService and the framework uses the default IRIS inbound adapter behind the scenes.

In my first article, I described the baseline version of the FHIR Patient Snapshot Agent: a Streamlit and Python application that retrieves FHIR resources from InterSystems IRIS for Health and uses an LLM to generate a concise patient summary.

This follow-up article explains how I extended the project with two additional InterSystems-focused features:

• Source context vector search
• Embedded Python artifacts for IRIS-compatible review

The goal was to make the project more useful as a clinical summarisation prototype while keeping the design small enough to understand and reproduce.

Hi Community,

In this article, I will introduce my application iris-fhir-agents A multi-agent clinical AI platform powered by InterSystems IRIS for Health. Features agents for triage, specialist consultation, pharmacy safety, and FHIR server exploration — all grounded by IRIS Vector Search RAG. Includes a no-code Agent Builder that lets you design and deploy custom clinical agents without writing a single line of code.

The successful construction and implementation of AI agents to address diverse use cases in the healthcare sector depend on high-quality data and APIs, effective governance, and management. The InterSystems IRIS FHIR server delivers all of this and is also fluent in Python, Vectors, and Interoperability. Combined with a strong LLM, patients, physicians, caregivers, and managers gain access to state-of-the-art technology for personal and public health.

Hi!

I'm trying to compiling a class with an Embedded Python and got this error:

Compilation started on 06/12/2026 11:44:17 with qualifiers 'cuk'
Compiling class User.VectorSearch
ERROR #7802: Worker job/s '749:33' unexpectedly shut down in group '#Default:(446070926892):0'.
ERROR #7812: Work queue unexpectedly removed, shutting down.
ERROR #5002: ObjectScript error: <THROW>WaitForComplete+215^%SYS.WorkQueueMgr *%Exception.StatusException ERROR #7802: Worker job/s '749:33' unexpectedly shut down in group '#Default:(446070926892):0'.
ERROR #7812: Work queue unexpectedly removed, shutting down.
Detected 3 errors during compilation in 1.020s.

1. Introduction

Epidemiological surveillance is one of the foundational pillars of public health. Régis Júnior et al. (2026) define it as a continuous system of data collection, analysis, interpretation and dissemination of health events — a function whose effectiveness depends critically on the quality of information systems, data analysis capacity, and coordination between different levels of care.

Hi Community,

Have you ever wished your EHR could think? Not just display data. Not just fire alerts. But actually read a patient record, reason over clinical guidelines, and write a structured referral order back to the system — in response to a single message from a clinician

In this article, I am going to show you how to create your own custom clinical AI agent.

🏥 About iris-fhir-agents App

iris-fhir-agents is a multi-agent clinical AI platform built entirely on InterSystems IRIS for Health.

In the previous article, we used pyprod to create production components while relying on the UI for production configuration. That same production can now be defined entirely in Python:

from intersystems_pyprod import Production, ServiceItem, ProcessItem, OperationItem

iris_package_name = "HelloWorld"

class MyProduction(Production):
    services = [
        ServiceItem(
            "MyServiceName",
            "HelloWorld.MyService",
            host_settings={"target": "MyProcessName"},
        )
    ]
    processes = [
        ProcessItem(
            "MyProcessName",
            "HelloWorld.MyProcess",
            host_settings={"target": "MyOperationName"},
        )
    ]
    operations = [
        OperationItem("MyOperationName", "HelloWorld.MyOperation")
    ]

In the previous IoP article, I showed how IoP can expose Python messages to DTL by generating JSON schemas. That is useful when the message is primarily a Python object and we want the IRIS tooling to understand its structure.

This time, the direction is a little different.

Starting with IoP 3.7.1, a PersistentMessage can now be a native IRIS message body class. The Python class is still the source code you write, but the generated IRIS class extends Ens.MessageBody