Safe, composable dataflow operators for Azure IoT Operations, built as WASM modules using the Component Model, WIT interfaces, and WASI Preview 2.
Evaluating AI agents for NL-to-SQL generation across Azure Databricks AI/BI Genie, GitHub Copilot CLI, and Microsoft Agent Framework. We achieved ~75% accuracy with schema documentation and runtime validation, while discovering that business logic errors represent a fundamental limitation requiring domain expertise.
A security-first architecture that propagates SharePoint document permissions into downstream AI systems, using Microsoft Entra ID object IDs (GUIDs) for safe, query-time filtering in Azure AI Search, RAG pipelines, and Copilot extensions.
Key insights from the EDA and Ground Truth journey for solving well identity resolution in the Energy industry—challenges that extend to any entity matching problem across distributed systems.
How to build an observable MCP Server with discoverable tools and agents.
A step-by-step guide to enable search in the semantic kernel agents using bing grounding in Azure Foundry AI Agent using A2A.
In this post we explore the evolution from Azure IoT Operations Data Processor Pipelines to Dataflows, why we adopted a hybrid strategy with custom Rust pods, and the architectural lessons we learned building event detection systems at the edge.
When setting up an experiment is complex, agents can automate the process to make it faster and more reliable.
How we built a production-ready MCP server with OAuth 2.1 authentication and On-Behalf-Of flow for Microsoft Graph, navigating a rapidly evolving specification.
Agentic systems have some discretion in the parameters they sent to tooling, but there are cases, such as experimentation, when you need 100% adherence to a set of parameters.
