Microsoft AB-100: MCP and Agent Extensibility
Agent extensibility is where an AI solution moves beyond conversation and begins interacting with real enterprise capabilities. In the Microsoft AB-100 context, Model Context Protocol […]
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Microsoft AB-100: Grounding and Knowledge Sources
Grounding is what turns a general-purpose model into an agent that can work with an organization’s actual information. For the Microsoft AB-100 exam, grounding is […]
Read articleMicrosoft AB-100: AI Governance and Security
AI governance for Microsoft AB-100 is not a policy document attached after the solution has been designed. It is part of the architecture itself. An […]
Read articleMicrosoft AB-100: ALM for Agents and Models
Application lifecycle management becomes more complicated when the application includes agents, prompts, models, knowledge sources and tools. For the Microsoft AB-100 exam, ALM is therefore […]
Read articleMicrosoft AB-100: Monitoring and Agent Telemetry
An agent can be online, return HTTP 200 responses and still be failing its business purpose. That is why monitoring for the Microsoft AB-100 exam […]
Read articleMicrosoft AB-100: ROI and Build-vs-Buy Decisions
AI architecture begins before any agent is built. For the Microsoft AB-100 exam, solution architects are expected to connect technical design to business value, including […]
Read articleMicrosoft AI-103: Foundry Service Selection
Service selection is one of the most important AI-103 skills because Microsoft Foundry is not a single feature. It is a platform that brings together […]
Read articleMicrosoft AI-103: RAG and Vector Search
Retrieval-augmented generation solves a practical problem: language models are powerful, but they do not automatically know an organization’s private data or the latest version of […]
Read articleMicrosoft AI-103: Building Agents in Foundry
An agent is more than a chatbot with a longer prompt. It combines a model with instructions, state, tools, knowledge and a runtime that allows […]
Read articleMicrosoft AI-103: Multi-Agent Orchestration
Multi-agent architecture is useful when a complex task can be decomposed into parts that benefit from different instructions, tools, context or execution timing. It is […]
Read articleMicrosoft AI-103: Securing AI Workloads
AI workloads introduce familiar cloud-security requirements and several new ones. Identity, network isolation, secrets management and least privilege still matter, but generative applications also consume […]
Read articleMicrosoft AI-103: AI Observability and Evaluation
Traditional application monitoring answers questions such as whether a service is available, how long requests take and how many errors occur. Generative AI systems add […]
Read articleMicrosoft AI-103: Multimodal AI Workflows
Multimodal AI works across more than one kind of input or output: text, images, audio, video and documents. The engineering challenge is not simply selecting […]
Read articleMicrosoft AI-103: Text and Speech Solutions
Text and speech workloads look familiar because they have existed in Azure AI for years, but AI-103 places them inside a newer application model. Language […]
Read articleMicrosoft AI-103: Content Understanding
Content Understanding is designed for a common enterprise problem: information arrives in formats that are easy for people to inspect but difficult for software to […]
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