I joined InterSystems less than a year ago. Diving into ObjectScript and IRIS was exciting, but also full of small surprises that tripped me up at the beginning. In this article I collect the most common mistakes I, and many new colleagues, make, explain why they happen, and show concrete examples and practical fixes. My goal is to help other new developers save time and avoid the same bumps in the road.

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InterSystems FAQ rubric

One way to optimize query performance is to use query parallelism on a per-query or system-wide basis (a standard feature).

This is a technique for dividing the execution of a particular query among processors on a multi-processor system. The query optimizer will execute parallel processing only if there is a possibility of benefiting from parallel processing. Parallel processing is only applicable to SELECT statements.

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In the previous article, we saw how to build a customer service AI agent with smolagents and InterSystems IRIS, combining SQL, RAG with vector search, and interoperability.

In that case, we used cloud models (OpenAI) for the LLM and embeddings.

This time, we’ll take it one step further: running the same agent, but with local models thanks to Ollama.

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We currently have Business Operation that we built to use the EnsLib.SQL.OutboundAdapter so we can make Microsoft SQL Server Stored Procedure calls. The BO is attached to a Java Gateway Service.

Some of our MS SQL Databases have moved from being OnPrem to Azure Cloud. We have started seeing where we are receiving errors on the BO saying that we cannot connect to the Azure Database, but we never receive a Disconnect from the Azure Database.

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Customer support questions span structured data (orders, products 🗃️), unstructured knowledge (docs/FAQs 📚), and live systems (shipping updates 🚚). In this post we’ll ship a compact AI agent that handles all three—using:

  • 🧠 Python + smolagents to orchestrate the agent’s “brain”
  • 🧰 InterSystems IRIS for SQL, Vector Search (RAG), and Interoperability (a mock shipping status API)

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