Category Archives: Microsoft
Device compliance and Conditional Access are powerful because they separate two jobs. Intune evaluates whether a managed device meets defined requirements; Microsoft Entra Conditional Access can use that compliance state as one signal in an access decision. The separation is also where mistakes occur: a compliance policy can be correct while the wrong users are […]
A security baseline is valuable because it collects a defensible starting posture, but it is not a substitute for understanding the controls it configures. Weak implementations appear when teams deploy a baseline as a one-time hardening event, layer custom profiles over the same settings, ignore exceptions, or assume a green policy state proves the endpoint […]
Application delivery becomes reliable when Intune can answer two questions consistently: should this app be installed here, and how can the platform prove the desired version is present? Packaging, assignments, dependencies, detection rules, supersedence, uninstall behavior, restart handling, and reporting all feed those two decisions. A deployment that installs successfully but cannot be detected correctly […]
A configuration policy represents intent; the endpoint runs the effective state produced after every relevant policy, local setting, security control, platform behavior, and timing condition has been evaluated. That gap between intent and effective state is where Intune policy management conflicts become confusing. Two profiles can target the same setting differently, a security baseline can […]
Windows Autopilot is most useful when it is treated as a provisioning lifecycle rather than a one-click imaging replacement. The service coordinates device identity, deployment profile, out-of-box experience, Microsoft Entra state, Intune enrollment and management, required policy, and application delivery. Each handoff can succeed or fail independently, which is why a good Autopilot design begins […]
Enrollment is not merely the act of making a device appear in the Intune console. It establishes management authority, device identity, ownership assumptions, user association, and the starting conditions for policies that arrive later. The right enrollment strategy therefore depends on where the device came from, who owns it, how it will be handed to […]
Microsoft AI-103: Production Monitoring for AI Apps Production AI failures rarely announce themselves as “the model is broken.” Users report slow answers, inconsistent citations, missing tool calls, rising costs, or a sudden increase in refusals. Those symptoms can originate in the model, the retrieval layer, an agent tool, identity, rate limits, safety policy, data ingestion, […]
Microsoft AI-103: Responsible AI and Content Safety at Runtime Responsible AI becomes real at runtime. Principles, review boards, and policy documents matter, but users experience the system through individual prompts, retrieved documents, generated outputs, tool calls, and escalation decisions. A production architecture therefore needs mechanisms that turn policy into observable behavior: content filtering, access control, […]
Microsoft AI-103: Prompt Orchestration and Evaluation A production generative AI application rarely has one prompt. It has system instructions, user content, retrieved evidence, tool definitions, conversation state, policy messages, templates, routing decisions, and sometimes several model calls that transform one another’s output. Prompt engineering becomes prompt orchestration: deciding which context appears where, which step owns […]
Microsoft AI-103: Multimodal AI Under Real Constraints Multimodal AI combines more than one kind of input or output—text, images, audio, video, documents, or structured data—so an application can reason across information that humans naturally consume together. The compelling demo is easy: attach an image and ask a question. The production problem is harder because each […]
Microsoft AI-103: Tool Use and Function Calling Function calling gives a model a structured way to request work from software outside the model. The model does not execute the business operation by itself; it selects a named capability and proposes arguments, while application code validates the request, invokes the real system, and returns the result. […]
Microsoft AI-103: RAG on Azure: A Practical Mental Model Retrieval-augmented generation is often summarized as “search first, then ask the model,” but that shortcut hides the decisions that determine whether the system is useful. A production RAG application has to ingest source material, segment it, represent it for retrieval, enforce access rules, interpret a user […]
Microsoft AI-103: Foundry Project Design When a Microsoft Foundry project does not behave as expected, changing the prompt is often the wrong first move. The failure may sit in resource configuration, identity, networking, model deployment, connection setup, tool permissions, quota, or the transition between older hub-based patterns and the newer Foundry resource model. A disciplined […]
Microsoft AI-103: Foundry Agent Architecture Agent diagrams are easy to draw. A box for a model, a box for tools, a box for knowledge, and arrows between them can make an architecture look complete long before the difficult decisions have been made. A useful design review challenges what the diagram hides: identity, state, failure domains, […]
Human Oversight and Escalation for AI Agents “Human in the loop” sounds like a safe design principle until a team has to specify exactly which human, at what moment, with what evidence, and with what authority. An approval inserted everywhere can destroy the value of automation. An approval inserted nowhere can let a plausible model […]
