Official Technical Resources: Microsoft (page 5)
Official Technical Resources · Microsoft: 312 entries in total; this page lists entries 61–75.
Microsoft Agent Framework Workflows Orchestrations - Handoff
In-depth look at Handoff Orchestrations in Microsoft Agent Framework Workflows.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Workflow Orchestration · Multi-agent Orchestration
Link last checked: 2026-09-24
Microsoft Agent Framework Workflows Orchestrations - Magentic
In-depth look at Magentic Orchestrations in Microsoft Agent Framework Workflows.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Workflow Orchestration · Multi-agent Orchestration
Link last checked: 2026-09-24
Microsoft Agent Framework Workflows Orchestrations - Sequential
In-depth look at Sequential Orchestrations in Microsoft Agent Framework Workflows.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Workflow Orchestration · Multi-agent Orchestration
Link last checked: 2026-09-24
Designing and implementing a GenAI gateway solution
This document serves as an essential guide for engineering teams tasked with designing and implementing a GenAI gateway solution. This document applies to both Azure OpenAI and LLMs in general
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Model Deployment Options · LLM Observability and Tracing
Link last checked: 2026-09-24
Key considerations for designing a GenAI gateway solution
Key considerations for designing a GenAI gateway solution
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Model Deployment Options · LLM Observability and Tracing
Link last checked: 2026-09-24
GenAI gateway reference architecture using APIM
The selection below describes the reference design for key GenAI gateway.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Model Deployment Options · LLM Observability and Tracing
Link last checked: 2026-09-24
Generative AI
This section serves as a comprehensive guide for launching, operating, and enhancing Generative AI applications in a production environment. It encapsulates essential capabilities, from delivering innovative experiences to ensuring robust security and privacy, and managing the lifecycle of AI solutions.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: AI Agent · Agent Frameworks
Link last checked: 2026-09-24
LLMOps - Operational management of LLMs
LLMOps is the collection of tools and processes that manages the end-to-end process of developing, deploying, and maintaining LLM-based applications.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Model Deployment Options · LLM Observability and Tracing
Link last checked: 2026-09-24
Monitoring Generative AI applications
As the adoption of generative AI applications continues to grow, so does the necessity for robust monitoring.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: LLM Observability and Tracing · Model Deployment Options
Link last checked: 2026-09-24
Operationalize LLM Security & Safety Evaluations using Azure AI Evaluations SDK
This article explains how to implement local safety and security evaluations using the Azure AI Evaluations SDK. It describes how to securely integrate these evaluations into CI/CD pipelines for continuous security validation in Azure DevOps.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Model Evaluation and Benchmarks · Large Language Model (LLM)
Link last checked: 2026-09-24
Security planning for LLM-based applications
This article discusses the Security planning for the sample Retail-mart application. It shows the architecture and data flow diagram of the example application.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: AI Governance and Regulation · Human-in-the-loop (HITL) · AI Agent
Link last checked: 2026-09-24
Evaluating the performance of LLM summarization prompts with G-Eval
Evaluating the performance of summarization prompts is a challenging concept. The normal comparison done by LLMs between the output and a ground truth is not feasible, as the answer is subjective and difficult to compare. One solution, known as G-Eval, uses GPT-4 to evaluate the quality of summaries without a ground truth. It performs evaluation across four dimensions: fluency, coherence, consistency, relevance; it then outputs numeric results, allowing for objective comparison between prompt iterations, and a cohesive and scientific experimentation process.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Model Evaluation and Benchmarks · Large Language Model (LLM)
Link last checked: 2026-09-24
A list of metrics for evaluating LLM-generated content
Evaluating the performance of machine learning models is crucial for determining their effectiveness and reliability. To do that, quantitative measurement with reference to ground truth output (also known as evaluation metrics) are needed. However, LLM applications are a recent and fast evolving ML field, where model evaluation is not straightforward and there is no unified approach to measure LLM performance. Several metrics have been proposed in the literature for evaluating the performance of LLMs. It is essential to use the right metrics that are suitable for the problem we are attempting to solve.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Model Evaluation and Benchmarks · Large Language Model (LLM)
Link last checked: 2026-09-24
Fostering appropriate reliance on GenAI
This article describes a framework that helps product teams identify, assess, and mitigate overreliance risk in AI products.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: AI Governance and Regulation · Human-in-the-loop (HITL) · AI Agent
Link last checked: 2026-09-24
Create an MCP Server knowledge source (preview)
Learn how to create an MCP Server knowledge source for agentic retrieval in Azure AI Search, which connects to any external Model Context Protocol server.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval-Augmented Generation (RAG) · Embedding · Vector Databases
Link last checked: 2026-09-24