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MS Foundry Developer Migration Checklist

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Microsoft Foundry recently implemented a variety of changes across various areas. To assist with these changes, a Developer Migration Checklist is provided below to help maneuver these changes.   1. Migrate to the Unified SDK 2.0 [] Replace all uses of the old azure-ai-agents package with azure-ai-projects 2.0. [] Update code to use AIProjectClient for model inference, agents, evaluations, memory, and tracing. [] Remove legacy preview flags and update any custom tool or MCP integrations. [] Validate that all tool schemas and agent configurations work under the new client.   2. Move Agents to the Foundry Agent Service (GA) [] Migrate existing agent deployments to the new Agent Service runtime. [] Update agent code to use the OpenAI Responses compatible interface. [] Reconfigure private networking, Entra RBAC, and tracing endpoints. [] Test agent behavior in the updated Agent Playground and tracing...
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What is Query Boosting, Weighting, and Thresholding?

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Query Boosting means increasing the importance of certain terms or fields in a search query so they influence the ranking more strongly. Sometimes not all parts of a query are equally important. For example: - In a product search, matching the title might matter more than matching the description. - In a document search, matching a keyword might matter more than matching the body text.   For example, if you search for: title:"machine learning"^3 description:"machine learning" The "^3" means “boost the title match 3× more than the description match.”     Weighting is the general idea of assigning different levels of importance to features, fields, or signals during ranking or scoring. Boosting is a type of weighting, but weighting can apply to: - Query terms  - Document fields  - Machine ‑ learning features  - User behavior signals (clicks, recency, popul...
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All About AI Foundry Agents

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In a recent episode of "Made for Dev" with Stephen Simon, I get into the world of AI Agents in Microsoft Foundry. I explain why agents are the "new microservices" and demonstrate how to build, configure, and orchestrate them using natural language.   What you’ll learn in this video: • What is an AI Agent? Understand the shift from LLMs that just answer questions to agents that execute business tasks [ 01:30 ]. • The "Physician" Analogy: Why an LLM is like a doctor who knows the theory but needs "tools" to actually take your blood pressure [ 02:05 ]. • Step-by-Step Demo: Watch Sam build a weather-forecasting agent from scratch in Azure AI Foundry, including tool integration via Logic Apps [ 04:54 ]. • Evaluation & Quality: How to interpret "Task Adherence" and "Intent Resolution" scores to ensure your agent is performing accurately [ 10:34 ]. • Advanced Orchestra...
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Copilot Studio vs. Microsoft Foundry

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One of the questions many tech leaders ask is “Which tool should I use for AI applications, Copilot Studio or Microsoft Foundry?”   Copilot Studio is a Low ‑ code, business ‑ friendly tool for the not-very technical user.  It’s fast to deploy and accessible at https://copilotstudio.microsoft.com   Microsoft Foundry (formerly known as Azure AI Foundry) is a Pro ‑ code tool designed to be more developer ‑ centric for the more technical user.  It offers full control, scalability, and is Azure native but accessed from a different URL ( https://ai.azure.com )   Summary    
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Handling Missing Data - Part 1

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One of the big topics I have been speaking about is “ Data Cleansing for Machine Learning ”.  A major part of the data cleansing process is filling in missing values.  These values could be missing for a variety of reasons (manually entered data, improper data collection, incorrect exception handling, etc.)  Missing data can be summed up into 3 categories: MAR: Missing at Random, for patterns tied to known variables (e.g., age, education) where someone did not want to answer a question. MCAR: Missing Completely at Random, for random glitches, accidental loss (i.e. sensor failure) MNAR: Missing Not at Random, describes a situation where data is missing because of the value of the missing data itself.   To counter the issue of missing data, several techniques are available to fill in missing data.  In my video , I discuss 3 of those techniques. Multiple Imputation by Chained Equations (MICE) handles numeric or...
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Mar '26 Regional Tech Events

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User Groups Mar 3: Ohio North Database Training Mar 11: Azure Cleveland Mar 19: GLUG .NET Mar 25: Cleveland C# User Group   Conferences Feb – Jun: Agent Camp Mar 16: Memphis Agent Camp  
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Databricks Q&A

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Yesterday I delivered a presentation on "Data Cleansing using Databricks".  Listed below are the questions that came up as well as the answers related to Databricks.   Does state transfer from one cell to another in the Databricks notebook? Yes, state does transfer from one cell to another, as long as you're running in the same notebook session and on the same cluster.   Can Databricks Jobs (aka "pipelines") be accessed through a notebook? Yes, you can fully interact with Jobs from a notebook using the Databricks REST API or the Databricks CLI. Some of these interactions from a notebook include: Trigger a job run Check job run status Cancel a run Retrieve job metadata   However, you can't directly "open" the Jobs UI from a notebook, and you can't modify job definitions without using the API   Listed below is an example for accessing a Job from a notebook import requests import json   token = dbutils.secre...
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