# Azure OpenAI

Published articles for Azure OpenAI.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## Start here: Azure SQL Foundations series

DevFeed: [Start here: Azure SQL Foundations series](<https://devfeed.tech/articles/start-here-azure-sql-foundations-series-23833.md>)

Original publisher: [Read original article](<https://devblogs.microsoft.com/blog/start-here-azure-sql-foundations-series/>)

Author: Anna Hoffman

Published: 2026-08-25T16:00:18Z

Content type: article

Language: en

Sources: [Developer Blogs](<https://devfeed.tech/sources/developer-blogs.md>)

Topics: [Azure SQL](<https://devfeed.tech/topics/azure-sql.md>), [Microsoft](<https://devfeed.tech/topics/microsoft.md>), [migration](<https://devfeed.tech/topics/migration.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Azure OpenAI](<https://devfeed.tech/topics/azure-openai.md>), [DevOps](<https://devfeed.tech/topics/devops.md>), [GitHub](<https://devfeed.tech/topics/github.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [API](<https://devfeed.tech/topics/api.md>), [MCP Server](<https://devfeed.tech/topics/mcp-server.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [azure](<https://devfeed.tech/tags/azure.md>), [azure-openai](<https://devfeed.tech/tags/azure-openai.md>), [azure-sql](<https://devfeed.tech/tags/azure-sql.md>), [devops](<https://devfeed.tech/tags/devops.md>), [github](<https://devfeed.tech/tags/github.md>), [guide](<https://devfeed.tech/tags/guide.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [json](<https://devfeed.tech/tags/json.md>), [langchain](<https://devfeed.tech/tags/langchain.md>), [mcp](<https://devfeed.tech/tags/mcp.md>), [microsoft-for-developers](<https://devfeed.tech/tags/microsoft-for-developers.md>), [migration](<https://devfeed.tech/tags/migration.md>), [powershell](<https://devfeed.tech/tags/powershell.md>), [rag](<https://devfeed.tech/tags/rag.md>), [terraform](<https://devfeed.tech/tags/terraform.md>)

### AI overview

Microsoft introduces the Azure SQL Database Foundations series, a set of four videos covering Hyperscale databases, modernization and migration, AI features, and operational data. Each episode includes a repository for hands-on follow-up.

### Source excerpt

Most developers I talk to aren't asking whether Azure SQL Database can handle their next app. They're asking where to start when it comes to modernization, migration, and AI in the database. If you're reading this, you're probably in a similar boat: you've got an existing schema or databases, a scaling question you haven't had [...] The post Start here: Azure SQL Foundations series appeared first on Microsoft for Developers.

## Browser automation with Pydantic AI + Playwright

DevFeed: [Browser automation with Pydantic AI + Playwright](<https://devfeed.tech/articles/browser-automation-with-pydantic-ai-playwright-21751.md>)

Original publisher: [Read original article](<http://blog.pamelafox.org/2026/08/browser-automation-with-pydantic-ai.html>)

Author: Pamela Fox (noreply@blogger.com)

Published: 2026-08-20T21:57:18Z

Content type: tutorial

Language: en

Sources: [Pamela Fox](<https://devfeed.tech/sources/pamela-fox.md>)

Topics: [Playwright](<https://devfeed.tech/topics/playwright.md>), [Pydantic](<https://devfeed.tech/topics/pydantic.md>), [Browser Automation](<https://devfeed.tech/topics/browser-automation.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Azure OpenAI](<https://devfeed.tech/topics/azure-openai.md>), [Entra ID](<https://devfeed.tech/topics/entra-id.md>), [Authentication](<https://devfeed.tech/topics/authentication.md>), [OAuth 2.0](<https://devfeed.tech/topics/oauth2.md>), [OpenTelemetry](<https://devfeed.tech/topics/opentelemetry.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [api-keys](<https://devfeed.tech/tags/api-keys.md>), [authentication](<https://devfeed.tech/tags/authentication.md>), [automation](<https://devfeed.tech/tags/automation.md>), [azure-openai](<https://devfeed.tech/tags/azure-openai.md>), [browser](<https://devfeed.tech/tags/browser.md>), [microsoft](<https://devfeed.tech/tags/microsoft.md>), [microsoft-foundry](<https://devfeed.tech/tags/microsoft-foundry.md>), [openai](<https://devfeed.tech/tags/openai.md>), [playwright](<https://devfeed.tech/tags/playwright.md>), [python](<https://devfeed.tech/tags/python.md>)

### AI overview

This tutorial explains how to combine Pydantic AI with Playwright to build agents that browse websites programmatically. It covers connecting Pydantic AI to Microsoft Foundry models through an OpenAI-compatible endpoint, using Entra token-based authentication, and applying Playwright for browsing, manual QA, and design iteration.

### Source excerpt

When we build agents, we often want to give them the ability to browse the web: open webpages, navigate from one page to the other, and read the content of a webpage. By combining Pydantic AI with the Playwright capability from Pydantic AI Harness, we can build agents that browse the web safely and programmatically. Using Pydantic AI with Microsoft Foundry models Pydantic AI is an open-source model-agnostic framework from Pydantic for building LLM-based applications and agents. It's type-safe and supports OpenTelemetry, making it a great choice for robust production applications. We can use Pydantic-AI with Microsoft Foundry models using either API keys or Entra token-based authentication. When possible, we always recommend the keyless route, so that's what we'll demonstrate here. We use the azure-identity package to authenticate with Entra, using either local or managed identity, and get back a token provider callback function for that credential: from azure.identity.aio import AzureDeveloperCliCredential, get_bearer_token_provider credential = AzureDeveloperCliCredential() token_provider = get_bearer_token_provider(credential, "https://cognitiveservices.azure.com/.default") Then we use the OpenAI package to configure the model connection: from openai import AsyncOpenAI client = AsyncOpenAI( base_url=os.environ["AZURE_OPENAI_ENDPOINT"] + "/openai/v1", api_key=token_provider, ) model = OpenAIChatModel( model_name=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT"], provider=OpenAIProvider(openai_client=client), ) Let's explain the options used above: base_url: We point this at the OpenAI-compatible endpoint for our Foundry model. This endpoint works for Azure OpenAI models (like gpt-5.4, which this project deploys), and for cross-provider Foundry models that support the OpenAI v1 API, like Kimi-K2.7-Code. The base URL looks like "https://AZURE_OPENAI_SERVICE_NAME.openai.azure.com/openai/v1". api_key: We pass in the token provider callback function that generates OAuth2 token

## Introducing SyGra Studio

DevFeed: [Introducing SyGra Studio](<https://devfeed.tech/articles/introducing-sygra-studio-7052.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/ServiceNow-AI/sygra-studio>)

Author: Surajit Dasgupta; Bidyapati Pradhan; Amit Kumar Saha; Vipul Mittal; Sriram Puttagunta

Published: 2026-02-05T16:52:28Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [ai observability](<https://devfeed.tech/topics/ai-observability.md>), [debugging](<https://devfeed.tech/topics/debugging.md>), [dashboards](<https://devfeed.tech/topics/dashboards.md>)

Tags: [azure-openai](<https://devfeed.tech/tags/azure-openai.md>), [bedrock](<https://devfeed.tech/tags/bedrock.md>), [cost](<https://devfeed.tech/tags/cost.md>), [debugging](<https://devfeed.tech/tags/debugging.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [json](<https://devfeed.tech/tags/json.md>), [latency](<https://devfeed.tech/tags/latency.md>), [llm](<https://devfeed.tech/tags/llm.md>), [observability](<https://devfeed.tech/tags/observability.md>), [openai](<https://devfeed.tech/tags/openai.md>), [vertex](<https://devfeed.tech/tags/vertex.md>), [vllm](<https://devfeed.tech/tags/vllm.md>), [workflow](<https://devfeed.tech/tags/workflow.md>)

### AI overview

SyGra Studio provides a guided interface for building and running LLM workflows. It connects data sources, configures models and structured outputs, and exposes execution status, costs, latency, guardrail outcomes, logs, and breakpoints.

### Source excerpt

- Configure and validate models with guided forms (OpenAI, Azure OpenAI, Ollama, Vertex, Bedrock, vLLM, custom endpoints). - Connect Hugging Face, file-system, or ServiceNow data sources and preview rows before execution. - Configure nodes by selecting models, writing prompts (with auto-suggested variables), and defining outputs or structured schemas. - Design downstream outputs using shared state variables and Pydantic-powered mappings.

## Introducing MCP Support in AI Shell Preview 6

DevFeed: [Introducing MCP Support in AI Shell Preview 6](<https://devfeed.tech/articles/introducing-mcp-support-in-ai-shell-preview-6-2996.md>)

Original publisher: [Read original article](<https://devblogs.microsoft.com/powershell/preview-6-ai-shell/>)

Author: Steven Bucher

Published: 2025-08-05T18:40:38Z

Content type: release

Language: en

Sources: [PowerShell Team](<https://devfeed.tech/sources/powershell-team.md>)

Topics: [AI Shell](<https://devfeed.tech/topics/ai-shell.md>), [Model Context Protocol (MCP)](<https://devfeed.tech/topics/model-context-protocol-mcp.md>), [MCP Server](<https://devfeed.tech/topics/mcp-server.md>), [PowerShell](<https://devfeed.tech/topics/powershell.md>), [Command-line interface](<https://devfeed.tech/topics/cli.md>), [Automation](<https://devfeed.tech/topics/automation.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-shell](<https://devfeed.tech/tags/ai-shell.md>), [automation](<https://devfeed.tech/tags/automation.md>), [azure-openai](<https://devfeed.tech/tags/azure-openai.md>), [command-line](<https://devfeed.tech/tags/command-line.md>), [copilot-in-azure](<https://devfeed.tech/tags/copilot-in-azure.md>), [mcp](<https://devfeed.tech/tags/mcp.md>), [model-context-protocol](<https://devfeed.tech/tags/model-context-protocol.md>), [powershell](<https://devfeed.tech/tags/powershell.md>), [release](<https://devfeed.tech/tags/release.md>)

### AI overview

AI Shell Preview 6 adds MCP client integration, built-in tools, command improvements, and aliases and flows for terminal use. The release explains how to connect MCP servers, inspect their status and available tools, and combine them with AI Shell's built-in capabilities.

### Source excerpt

We're excited to share the latest preview release of AI Shell that includes new features and improvements based on your feedback. The post Introducing MCP Support in AI Shell Preview 6 appeared first on PowerShell Team.

## Agent-Facing Analytics, Data Lake Support, and More: A Year of ClickHouse Cloud on Azure

DevFeed: [Agent-Facing Analytics, Data Lake Support, and More: A Year of ClickHouse Cloud on Azure](<https://devfeed.tech/articles/agent-facing-analytics-data-lake-support-and-more-a-year-of-clickhouse-cloud-on-azure-4907.md>)

Original publisher: [Read original article](<https://clickhouse.com/blog/a-year-of-clickhouse-cloud-on-azure>)

Author: ClickHouse

Published: 2025-05-15T00:00:00Z

Content type: release

Language: en

Sources: [ClickHouse Blog](<https://devfeed.tech/sources/clickhouse-blog.md>)

Topics: [clickhouse](<https://devfeed.tech/topics/clickhouse.md>), [Azure](<https://devfeed.tech/topics/azure.md>), [Azure OpenAI](<https://devfeed.tech/topics/azure-openai.md>), [Model Context Protocol](<https://devfeed.tech/topics/model-context-protocol.md>), [Model Context Protocol (MCP)](<https://devfeed.tech/topics/model-context-protocol-mcp.md>), [LLM observability](<https://devfeed.tech/topics/llm-observability.md>), [real-time](<https://devfeed.tech/topics/real-time.md>), [Low Latency](<https://devfeed.tech/topics/low-latency.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>)

Tags: [azure](<https://devfeed.tech/tags/azure.md>), [azure-openai](<https://devfeed.tech/tags/azure-openai.md>), [clickhouse](<https://devfeed.tech/tags/clickhouse.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [llm-observability](<https://devfeed.tech/tags/llm-observability.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [model-context-protocol](<https://devfeed.tech/tags/model-context-protocol.md>), [real-time](<https://devfeed.tech/tags/real-time.md>)

### AI overview

The article reviews a year of ClickHouse Cloud on Microsoft Azure and announces expanded support for AI ecosystems, data lakes, onboarding, programming languages, cloud architecture, enterprise security, and compliance. It highlights Azure OpenAI and Model Context Protocol integrations for agent-facing real-time analytics, as well as LLM observability and Airflow pipeline insights.

### Source excerpt

Read on to learn more about our latest product announcements for ClickHouse Cloud on Azure, from AI ecosystems and data lake support, to data onboarding, and more.

## Giving our AI superpowers with OpenAI Tools

DevFeed: [Giving our AI superpowers with OpenAI Tools](<https://devfeed.tech/articles/giving-our-ai-superpowers-with-openai-tools-40844.md>)

Original publisher: [Read original article](<https://mutto.fyi/posts/2024/06/ai-superpowers-tools/>)

Published: 2024-06-03T00:00:00Z

Content type: tutorial

Language: en

Sources: [Mutt0-ds Notes](<https://devfeed.tech/sources/mutt0-ds-notes.md>)

Topics: [OpenAI](<https://devfeed.tech/topics/openai.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [function calling](<https://devfeed.tech/topics/function-calling.md>), [Azure OpenAI](<https://devfeed.tech/topics/azure-openai.md>), [Databases](<https://devfeed.tech/topics/databases.md>), [API](<https://devfeed.tech/topics/api.md>), [SQL](<https://devfeed.tech/topics/sql.md>), [LangChain](<https://devfeed.tech/topics/langchain.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-tools](<https://devfeed.tech/tags/ai-tools.md>), [api](<https://devfeed.tech/tags/api.md>), [azure-openai](<https://devfeed.tech/tags/azure-openai.md>), [function-calling](<https://devfeed.tech/tags/function-calling.md>), [langchain](<https://devfeed.tech/tags/langchain.md>), [openai](<https://devfeed.tech/tags/openai.md>), [retrieval-augmented-generation-rag](<https://devfeed.tech/tags/retrieval-augmented-generation-rag.md>)

### AI overview

This article explains how the author used OpenAI Tools, also called Function Calling, with Azure OpenAI to build an AI Analyst connected to a complex order database. It contrasts this approach with earlier GPT-3.5, LangChain, SQL, and DAX-based methods, and distinguishes tool calling from Retrieval Augmented Generation.

### Source excerpt

In recent months, I have been experimenting with AI tools to leverage new, powerful technologies and create value. Like you, I am inundated...