Cloud Native Platforms: Evolve
Audience: Engineering leaders, platform architects, senior developers exploring how to operationalise AI in their teams Reading time: 8 minutes Series: Cloud Native Platforms. Build, Run, Evolve. This is Part 3 of 3. Cloud helped us scale infrastructure. AI is starting to do the same thing for the work around the code: the planning, the testing, the release communication, the incident triage, the writing that surrounds writing software. The conversation about AI in software has narrowed too quickly to "Copilot in the editor". The bigger story is happening across the lifecycle. Planning, design, development, testing, release, and operations are all being augmented at once. The platforms that adopt AI well are not the ones with the most usage. They are the ones with the clearest discipline around how it is used. This post is about that discipline. AI is changing how we engineer, not how we type AI is not changing how we write code. It is changing how we engineer software. Code generation is the surface. Underneath it, AI is reshaping the unit of leverage. The question is no longer how fast a developer can type. It is how well a workflow can be expressed as a reusable engineering asset. Six disciplines determine whether AI moves the needle on outcomes or just adds another tool to the stack. Figure 1. AI across the SDLC. Each phase has clear AI assist points and clear human-owned validations. The boundary is not negotiable. It is the design. 1. From assistance to augmentation Early AI tools focused on assisting individual developers. Code suggestions. Autocomplete. Quick refactors. The value was real but bounded by the editor. The shift now is into structured workflows that span the lifecycle. The unit of leverage is no longer a single suggestion. It is a sequence of actions executed reliably across phases. ("Agentic" later in this post means a system that makes its own next-step decisions inside guardrails. A workflow follows a fixed sequence; an agent chooses the path.) Code generation has become baseline, not differentiator Workflow generation is where the largest gains live Multi-step assistance with explicit human checkpoints Context that travels across tools, not just within one In practice The pattern that works: start with the single highest-volume writing task on the team (commit messages, code review comments, release notes, postmortem first drafts) and turn the AI assist for that task into a shared workflow rather than each individual's private trick. The cost is one engineer's afternoon documenting the workflow and the eval set. The return is that every engineer on the team inherits the work, and the task that used to consume an engineer's morning every two weeks becomes a background step in the release process. Workflow generation, not faster typing, is where the gains compound across a team. Code suggestions help one developer. Reusable workflows help the next ten. 2. AI across the SDLC, with guardrails AI now has a useful role at every phase of delivery. The role is different at each phase, and the guardrails are different too. Phase What AI helps with What humans must validate Plan Breaking down requirements, drafting acceptance criteria Domain context, business priorities, customer impact Build Code generation, refactoring, scaffolding Architectural fit, security boundaries, performance Test Test case generation, edge case discovery Coverage of business-critical paths, regulatory cases Release Release notes, changelog summaries, communication drafts Accuracy, tone, customer-facing claims Operate Log triage, incident summaries, runbook drafts Root cause attribution, action item ownership The guardrails are not optional decoration. They are the design. In practice The pattern that works: stage AI assists for release communication (changelog drafting, customer-facing release notes, internal release announcements) and require a human review before anything goes out. The draft arrives consistently, faster than a human could produce, and easier to compare across releases. The reviewer is not eliminated; the reviewer is moved from author to editor, which is where their judgment actually matters. Teams that adopt this pattern stop missing release-note deadlines and stop publishing inconsistent communication across products. 3. From prompts to reusable assets Many teams begin with prompt experimentation. Individuals find techniques that work for their tasks. The result is a patchwork of personal practices that do not survive a team change. The compounding value comes when prompts mature into reusable engineering assets. Figure 2. The maturity model from prompts to agents. The value compounds at the workflow stage and accelerates at the agent stage. The disciplines that make agents safe are the same ones that made workflows reliable. The maturity stages, in order of leverage: Prompts: ad-hoc, individual, hard to share Templates: parameterised prompts versioned with the project Workflows: multi-step sequences with clear inputs, outputs, checkpoints Agents: autonomous task chains operating within explicit guardrails The diagram is a maturity ladder, not a graduation. In practice teams operate at all four stages simultaneously for different tasks. A senior engineer may use a one-off prompt to explore a refactor, run a versioned template for commit messages, hand off to a workflow for release notes, and trigger an agent for routine PR triage, all in the same hour. The point of the ladder is not to leave earlier stages behind. It is to know which stage a given task belongs to and to invest accordingly. In practice The pattern that works: pick the three prompts your team uses every week, codify them as parameterised templates in the same repository as the application code, and treat them as engineering artefacts (reviewed, versioned, owned). New engineers inherit the team's accumulated practice instead of building their own from scratch. Quality becomes consistent because the variance between individuals shrinks. Investment pays back in weeks, not quarters, and the maturity ladder keeps producing returns as the team moves from templates to workflows to agents. 4. Agentic delivery, with guardrails that survive a security review The next stage is agentic. AI executes sequences of tasks within a defined scope. The risk is not that the agent will fail. It is that the system around the agent will not catch the failure, and that the failure modes are different in kind from traditional automation. Agents are non-deterministic, they can be manipulated through their inputs, and their actions can have side effects in systems the team does not own. Five guardrails make agentic delivery safe. The first four are necessary. The fifth is what carries the agent through a security review at a regulated enterprise. Identity and scope: the agent runs as a managed identity (or scoped service principal) with the smallest set of permissions that lets it do its job. Permissions are expressed as allowlists, not denylists. Tools fetched at runtime are subject to the same identity boundary as the agent itself. Input quarantine: anything the agent reads from a user-controlled source (work item bodies, PR descriptions, customer tickets) is treated as untrusted text. The agent does not execute instructions found in fetched content, and tool calls are validated against an output schema before execution. This is the prompt-injection mitigation, and it is the most common gap in agentic systems shipped today. Cost and blast-radius caps: every run has a maximum token budget, a maximum number of tool calls, and a maximum spend. Exceeding any cap aborts the run cleanly. Without caps, scoped credentials are not enough to bound the damage. Evaluations and traceability: agents are evaluated against a fixed test set before deployment, and on every prompt or model change. Every action is logged with inputs, outputs, the model and prompt versions used, and the reasoning trace where the model exposes one. Logs are redacted for secrets and personally identifiable information at write time. Reversibility taxonomy: actions are categorised by reversibility, not asserted to be reversible in general. A draft write to a private store is reversible. A post to a customer-facing channel is not reversible (deletion does not unsend). A database update may be reversible by a compensating transaction or not at all. Irreversible actions require human approval at the boundary, before they happen, not after. The agent is allowed to draft and stage. The human is the only one who is allowed to make the move that cannot be undone. In practice The pattern that works: start with one low-risk agent (release-notes drafter, PR triage assistant) running on read-only inputs, write-only-to-drafts permissions, and a hard cost cap per run. Require explicit human approval at the irreversible step. Wire up an evaluation set on day one, and rerun it on every prompt or model change. Treat regressions as failures, not warnings. The first agent the team ships is rarely the most valuable; it is the rehearsal that establishes the controls every later agent inherits. Teams that skip this rehearsal end up with an agent in production that no one feels safe extending. Implementation note An agent without a reversibility taxonomy and a regression eval set is a liability. The discipline is the same one that made workflows reliable: scoped identity, idempotency, traceability, and a clear boundary between machine action and human decision. The YAML below is illustrative, not a runtime contract; it is meant to show the shape of the controls a real agent definition would carry, not the syntax of any specific platform. # Agent run definition (illustrative; not a specific platform's syntax) name: release-notes-drafter trigger: pre-release identity: type: managed-identity scope: tenant=<tenant-id> resource=release-tools/<app-id> permissions: allow: - read: work-items in milestone (filter: state=Done) - read: pull-requests in milestone (filter: merged) - write: drafts/release-notes/${run-id} # Production channels are NOT in the allowlist. The agent cannot post. limits: max_tokens_per_run: 80000 max_tool_calls_per_run: 20 max_runtime_seconds: 300 max_cost_usd: 0.40 on_exceeded: abort_with_partial_artifact input_handling: treat_fetched_content_as: untrusted # Indirect prompt injection is mitigated by the layered discipline below, # not by a single feature flag. Each item is a separate control. enforce_instruction_hierarchy: true validate_tool_args_against_schema: true validate_outputs_against_schema: true steps: - fetch: completed work items in milestone - draft: release notes from items - validate: required fields present - request-review: from: release-manager idempotency_key: ${milestone-id}-${draft-hash} - on-approval: action: post-to-internal-channel reversibility: not-reversible requires: explicit-human-click # the agent does NOT click this audit: log_inputs: true log_outputs: true redact: - secrets # Pattern-based: handles structured PII like emails, phones, IDs. - pii_patterns: [email, phone, national-id, payment-card, ip-address] # Entity-based: required for unstructured PII like names. Pattern alone # cannot redact a customer name without an entity-recognition step. - pii_entities: ner-based # names, locations, organisations retain: 365_days # tune to your audit policy, not to the demo evaluation: test_set: tests/release-notes/eval-v3.jsonl on_prompt_change: rerun on_model_change: rerun fail_threshold: 5_percent_regression 5. Where AI still needs human judgment AI has clear boundaries. The boundaries are not embarrassing. They are the design. What must stay human-owned: Architectural trade-offs and design decisions Security validation and threat modelling Correctness for business-critical and regulatory paths Domain context that has not been written down Accountability for outcomes, not just outputs The goal is collaboration, not replacement. The teams that get the most value from AI are not the ones with the most automation. They are the ones with the clearest sense of where automation ends and judgment begins. In practice The pattern that works: name the human-owned items explicitly in the team's working agreement (architecture, security, regulatory correctness, accountability) and audit every AI workflow against that list. When a workflow asks the AI to make a decision in any of those categories, redesign it so the AI prepares the analysis and a human makes the call. Most teams over-trust AI for one of these areas in their first six months and learn the hard way. Naming the boundary up front prevents the lesson from being paid in production. The clarity is the value; the model behind the workflow is interchangeable. 6. Responsible AI is engineering work The first five disciplines decide whether AI moves the needle. The sixth decides whether the platform can defend the choices it makes with AI. Responsible AI is the engineering practice of building systems whose AI behaviour is fair, transparent, accountable, and safe by design, not by audit after the fact. Treating it as a compliance checkbox at the end of the project is how teams end up shipping AI workflows that fail security review, embarrass the company, or harm users. Six controls turn responsible AI from a policy into engineering work. These map directly onto the practices Microsoft and the broader industry have converged on, but the names matter less than the practice they enable. Fairness in inputs and outputs. The training data, eval set, and prompts are reviewed for systematic bias against any group the system serves. The eval set covers under-represented cases by design, not by accident, and regressions on those cases fail the build. Transparency to end users. When a user sees AI-generated content, they are told. When a decision is AI-assisted, the path from input to output is explainable in plain language, not just in a model card buried in documentation. Content safety filters. Inputs and outputs pass through safety classifiers (prompt injection, prohibited content, jailbreak patterns) before reaching the model and before reaching the user. Filtering decisions are logged and reviewable. Accountability ownership. Every AI workflow has a named owner who is accountable for its outcomes, not just its uptime. The owner has the authority to pause or roll back the workflow when harm is detected. Data minimisation and residency. The AI sees only the data it needs to do the task. Personally identifiable information and customer data are scoped, redacted, and kept inside the boundary the customer agreed to. Cross-tenant leakage is treated as a P1 incident, not a feature request. Harm evaluation alongside quality evaluation. The eval set measures harm potential (toxicity, hallucination on factual queries, leakage of confidential context) with the same rigour as it measures correctness. Both must pass for a release to ship. Figure 3. Responsible AI as a set of engineering controls around the AI workflow. The six controls fall into four categories: data discipline (fairness, data minimisation), model discipline (content safety, harm evaluation), deployment discipline (transparency to users), and governance (accountability ownership). All six are necessary; none is sufficient on its own. In practice The pattern that works: write the responsible AI plan before the first agent ships, not after the first incident. Pick one workflow that touches user data or generates customer-facing content, and use it as the reference implementation: fairness review on the eval set, content safety filters wrapping the model call, transparency annotation in the UI, redaction of identifying details in logs, harm evals running alongside quality evals on every change, and a named owner with explicit pause authority. The first such workflow takes longer to ship than the unconstrained version. Every workflow after it inherits the controls and ships faster than it would have without them. Teams that defer responsible AI to a future quarter end up retrofitting it under pressure, which is the most expensive way to do it. A scenario that ties it together Picture a platform team several months into using Copilot. Adoption is high. Productivity dashboards show gains. But defect rates are not improving and lead time is flat. Leadership asks the obvious question: is AI actually helping, or just feeling like help? The answer is not to stop using AI. It is to change how AI is measured. Move adoption metrics to the background. Move outcome metrics to the front: defect escape rate, lead time for change, change failure rate, mean time to recovery. In parallel, promote the individual prompts that have proved themselves to shared templates, and the templates to versioned workflows. Retrofit responsible AI controls onto the workflows that shipped first: content safety filters, harm evaluations alongside quality evaluations, transparency annotations on customer-facing output, and a named owner for each workflow. Six months later, the picture is different. Defect rate improves on the parts of the codebase where reusable workflows were introduced. Onboarding for new engineers is visibly faster. Release notes are consistent across teams. The shift is from celebrating use to tracking outcomes, and once the team measures what matters, the tooling decisions start making themselves. What teams get wrong The common pattern is measuring AI by usage, not by outcome. Adoption metrics tell you who tried Copilot. They do not tell you whether defects dropped, lead time improved, or release notes got better. The fix is not less AI. It is better measurement. The four metrics named in the scenario above (defect escape rate, lead time for change, change failure rate, mean time to recovery) come from the DORA research on software delivery performance and have become a useful default. Two warnings travel with them. First, attribution is hard: an AI workflow rolled out alongside a test refactor and a CI pipeline change cannot claim credit cleanly. Second, baselines matter more than headlines: a single quarter's improvement is not a trend, and a single team's gain is not the platform's gain. Outcome measurement done well needs a baseline window, an attribution discipline, and a kill criterion for workflows that are not paying back. Done poorly, it is just adoption metrics with better names. There is also the question of cost. AI usage carries a per-run token bill, an evaluation bill on every change, and (for agents) a cost cap that limits damage when something goes wrong. None of these are large compared to the engineering time saved when the workflow works. All of them are visible enough that a finance-aware reader will ask. Track them. Where to start The most concrete starter from this post: promote one personal prompt to a shared template. Pick the prompt that gets used most often (commit messages, code reviews, release notes, debugging assist), move it from someone's notes into the repository where the team versions everything else, and watch what changes when the next person on the team runs it. That is the smallest unit of the workflow shift this post argues for, and it is the step where prompts stop being individual practice and start becoming engineering assets. The shift The shift is from building systems to building smarter systems: AI does not replace engineers. It changes what an engineer's leverage looks like. The unit of value is the workflow, not the suggestion. The discipline that made platforms operable is the same discipline that makes AI useful. Responsible AI is not a compliance step. It is the sixth engineering discipline that lets the other five compound safely. The series ends here, but the arc is consistent across all three posts. The disciplines that make platforms scale are the same disciplines that make AI useful. Build with discipline. Run with discipline. Evolve with discipline. The tools change. The disciplines do not. Want to discuss? Where has AI moved the needle most in your delivery, and where has it disappointed you? Drop a comment with patterns you have seen in your environment. Every reply gets read. Previously in this series: Building Cloud Native Platforms That Scale: Patterns That Actually Work. Part 1 covered the design choices that make scale possible. Running Cloud Native Platforms: Why Day 2 Decides Everything. Part 2 covered the operational disciplines that decide production outcomes. This is the third and final post in the series.
OneDrive Photos Restyle with AI-now rolling out on mobile and web
Photos capture real moments. With AI Restyle in OneDrive, you can reimagine them in fresh new styles-right where your photos already live. Meet AI Restyle Photos capture real moments. With AI Restyle in OneDrive, you can reimagine those moments in expressive new styles-right where your photos already live. With just a tap, transform everyday photos into cinematic posters, hand‑painted artwork, pencil sketches, anime‑inspired scenes, and more. Choose a style, watch a new version appear in seconds, and keep exploring until it feels just right. Through it all, the people, places, and memories you care about stay unmistakably yours-just seen in a fresh new light. Your photos stay private When you use AI Restyle in OneDrive, your photos remain under your control and are processed only to generate the style you choose. For more information on how AI Restyle works, its intended uses, and limitations, see Transparency note for AI Restyle in OneDrive - Microsoft Support. What you can do with AI Restyle Create something beautiful instantly. Choose from a rotating set of one‑tap styles designed to match the content of your photo-so it’s easy to get a great result right away. New styles are added regularly, giving you fresh ways to reimagine your photos. Add a personal touch when you want. Include an optional prompt to guide the look-no design skills required. Explore until it feels right. Try multiple restyles, undo or redo changes, and keep experimenting until you find the look you love. Share in just a few taps. Go from viewing to restyling to sharing with your favourite apps-without ever leaving OneDrive Photos. Availability AI Restyle is rolling out on OneDrive for iOS, Android, and web for customers with a Microsoft 365 Premium subscription. Availability may vary by region as rollout continues. What’s next We’re continuing to expand AI-powered photo experiences in OneDrive-bringing AI Restyle to additional platforms and investing in new editing capabilities that help you create with confidence while keeping your photos authentic. Try it today Open OneDrive on iOS, Android, or web, sign in with a Microsoft 365 Premium account, open a photo, and tap on ‘AI Restyle’ to start exploring new styles. Have fun creating something new today! Try it on the OneDrive mobile app. iOS: Download Microsoft OneDrive from the App Store Android: Download Microsoft OneDrive from Google Play We’d love your feedback-use 👍👎 to help us improve AI Restyle. #Microsoft #OneDrive #Photos #iOS #Android #Web #AI * This blog was updated on April 7, 2026 to inform how AI Restyle in OneDrive protects users’ privacy and ensures their photos remain secure and under their control.
From Prompt to Production: Building Azure Architecture Diagrams with AI
Author: Arturo Quiroga, Senior Partner Solutions Architect — Microsoft Cloud architects spend significant time translating ideas into architecture diagrams. They toggle between Visio, draw.io, pricing calculators, and documentation. According to the 2024 Stack Overflow Developer Survey, 61% of developers spend more than 30 minutes a day searching for answers or solutions, time lost to context-switching rather than design. What if you could describe your architecture in plain English and get a diagram, cost estimate, and deployment guide in minutes? The Challenge: Fragmented Architecture Workflows Designing Azure architectures today typically involves multiple disconnected steps: Sketch the architecture in a diagramming tool Look up official Azure icons and drag them into place Research pricing across regions using the Azure Pricing Calculator Validate the design against the Well-Architected Framework (WAF) Write deployment documentation and Infrastructure as Code templates Compare alternative designs manually Each step lives in a different tool, and keeping them in sync as designs evolve is costly. The Azure Architecture Diagram Builder brings these workflows together in a single browser-based experience. How It Works Describe your architecture in natural language, for example "A HIPAA-compliant healthcare platform with FHIR APIs, event-driven processing, and multi-region disaster recovery", and the AI generates a diagram with grouped services, data flow connections, and logical organization. Figure 1. Enter a natural-language prompt describing your architecture. Curated example prompts help you get started, and you can optionally upload an existing diagram for the AI to analyze. The tool uses Azure OpenAI to power generation across multiple models, enabling you to choose the model that best fits your scenario — from fast iterations to deeper reasoning. Key Features AI-Powered Architecture Generation Describe what you need in plain English, and the AI creates an architecture diagram with: 714 official Azure service icons across 29 categories Smart grouping: services are logically organized (Frontend, Backend, Data, Security) Data flow connections: labeled edges showing how data moves through the system 13 curated example prompts: from simple web apps to complex enterprise scenarios like Zero Trust networks, Industrial IoT with 5,000+ sensors, and global multiplayer gaming backends Figure 2. A generated industrial IoT architecture. Top: the clean diagram view as initially produced. Bottom: the same diagram with per-service monthly cost overlays toggled on, plus a running subscription total in the toolbar. Architecture Image Import Already have an architecture on a whiteboard or in a screenshot? Upload the image and let the AI analyze it, mapping services to official Azure icons and recreating the architecture as an editable, interactive diagram. Figure 3. Upload a photo of a whiteboard sketch (top-right reference panel) and the AI recreates it as an editable diagram with official Azure service icons and labeled data flow connections. ARM Template Import Import existing ARM templates to visualize your current infrastructure. The AI parses resource definitions and dependencies, groups related resources into logical layers, and produces a meaningful diagram of what you actually have deployed — a fast way to document an inherited environment or sanity-check a template before deployment. Figure 4. ARM template import in action. Top: the parser status banner while resources and dependencies are being analyzed. Bottom: the resulting diagram, with resources auto-grouped into logical layers (Web Tier, Data Layer, Container Platform, Observability & Logging) and a Generated from: ARM Template badge linking the diagram back to its source file. Well-Architected Framework Validation Validate your architecture against all five WAF pillars — Security, Reliability, Performance Efficiency, Cost Optimization, and Operational Excellence. The validator provides: An overall WAF score with pillar-level breakdowns Specific findings with severity levels Actionable recommendations you can select and apply Select the recommendations you agree with, and the AI regenerates an improved architecture incorporating those changes. Figure 5. WAF validation results showing the overall score, per-pillar breakdowns, and individual findings with severity badges. Tick the recommendations you want and the AI rebuilds the diagram with those changes applied. Multi-Model Comparison Run the same architecture prompt through multiple AI models side-by-side and compare: Architecture Comparison: service counts, connection counts, groups, token usage, and latency Validation Comparison: WAF scores across models, severity breakdowns, and finding counts Apply Winner: pick the best result and apply it to the canvas with one click Present Critique: a talking avatar narrates the AI-generated ranking with live closed captions Figure 6. Multi-model comparison. Top: select the models and reasoning effort, then enter the prompt. Bottom: side-by-side results across all selected models with service counts, latency, token usage, and Fastest / Cheapest / Most Thorough badges. Multi-Region Cost Estimation Get cost estimates from the Azure Retail Prices API across 8 Azure regions: East US 2, Australia East, Canada Central, Brazil South, Mexico Central, West Europe, Sweden Central, and Southeast Asia. Features include: Color-coded cost legend (green / yellow / red thresholds) SKU and tier information for each service Export options: CSV, JSON, plain-text summary, and an analysis report with top cost drivers, Reserved Instance flags, and a ranked multi-region comparison table Figure 7. The cost legend overlay shows per-service pricing with color-coded thresholds. The region selector in the toolbar lets you re-price the entire architecture in any of eight Azure regions. Deployment Guide Generation with Bicep Generate step-by-step deployment documentation including: Prerequisites and Azure resource requirements Step-by-step deployment instructions Bicep templates for each service (Infrastructure as Code) Post-deployment verification steps Security configuration recommendations Figure 8. Each generated Deployment Guide opens with the architecture name, an estimated deployment time, and a prerequisites checklist covering subscription roles, CLI versions, Microsoft Entra ID permissions, and region requirements, followed by numbered, copy-ready deployment steps. Figure 9. The Infrastructure as Code section produces a main.bicep orchestrator plus a per-service module (Log Analytics, Key Vault, Cosmos DB, SQL Database, Event Hubs, Azure Functions, and more). The Download All Templates button packages everything into a ready-to-deploy folder. Workflow Animation & Avatar Presenter Visualize how data flows through your architecture with step-by-step animations that highlight services on the canvas as each step plays. When the Azure Speech Service is configured, a photorealistic talking avatar can narrate the workflow or present model comparison results, with live word-by-word closed captions in a draggable, resizable panel. Figure 10. A workflow step is highlighted on the canvas as the Avatar Presenter narrates that step. Live word-by-word closed captions appear in a draggable, resizable panel, useful for accessibility and stakeholder demos. Export Options Figure 11. A single-slide PowerPoint export, available in dark or light theme, ready to drop straight into a stakeholder deck. Format Use Case PNG Documentation, presentations SVG Scalable vector graphics PPTX Single PowerPoint slide (dark or light theme) Draw.io Edit in diagrams.net JSON Backup, version control CSV / ZIP Cost analysis with multi-region comparison Highlights The Azure Architecture Diagram Builder unifies the architecture design lifecycle in a single tool: End-to-end workflow: from natural-language description to deployable Bicep templates without tool switching Official Azure icons: 714 icons across 29 categories, mapped directly from the Azure service catalog Live pricing: queries the Azure Retail Prices API at design time rather than relying on static estimates WAF-integrated validation: architectural best practices built into the design loop rather than applied after the fact Multi-model flexibility: choose the AI model that best suits each task, with fast models for iteration and reasoning models for complex designs Open source: the source code is available for customization and contribution One-Command Deploy with Azure Developer CLI The fastest way to get your own instance running is with azd : # Install azd (once) brew tap azure/azd && brew install azd # macOS winget install microsoft.azd # Windows # Clone, configure, and deploy git clone https://github.com/Arturo-Quiroga-MSFT/azure-architecture-diagram-builder cd azure-architecture-diagram-builder azd auth login azd env set AZURE_OPENAI_ENDPOINT "https://your-resource.openai.azure.com/" azd env set AZURE_OPENAI_API_KEY "your-key" azd up # Provisions infrastructure + builds + deploys (~8 min) azd up provisions the following via Bicep: Resource Purpose Azure Container Registry Stores the Docker image Azure Container Apps Runs the app (nginx + token server) Log Analytics + Application Insights Monitoring and telemetry Azure Speech (S0) Avatar Presenter (optional, keyless auth via managed identity) Try It Today The Azure Architecture Diagram Builder is available now: Live demo: https://aka.ms/diagram-builder Source code: GitHub repository Documentation: See the Getting Started Guide for detailed setup instructions We welcome feedback and contributions. Use the GitHub Issues page to report bugs, suggest features, or share your experience. Tags: artificial intelligence · application · apps & devops · well architected · infrastructureWAR, Azure Advisor, and Us (Azure Arch Diagram Builder): Three Ways to Score an Azure Architecture
Author: Arturo Quiroga, Azure AI services Engineer - Senior Partner Solutions Architect — Microsoft A few days ago I published From Prompt to Production: Building Azure Architecture Diagrams with AI, introducing the open-source Azure Architecture Diagram Builder. One feature got more follow-up questions than any other: the Well-Architected Framework (WAF) validation. Architects from partners and customers — many of whom already use Azure Advisor and the Well-Architected Review — wanted to know exactly what scoring algorithm we use, how it compares to Microsoft's official tools, and whether they should be using all three. This post is that answer. It's a deep dive into how design-time WAF validation works, how Microsoft's two official WAF assessment algorithms work, and where each fits in the architecture lifecycle. TL;DR. Microsoft ships two WAF assessment vehicles — the Well-Architected Review (questionnaire, scored from human answers) and the Azure Advisor score (healthy-resources-÷-applicable-resources weighted per subcategory, with Defender Secure Score for Security and cost-weighted math for Cost). Both require either a human filling in a form or live Azure telemetry. Our app runs at design time on a diagram, before anything is deployed, using a hybrid pipeline: a deterministic rule pre-scan followed by an LLM refinement pass. Same five WAF pillars, different lifecycle stage. Complementary, not competitive. Why design-time validation matters Every cost overrun, reliability gap, and security incident I've ever debugged was cheaper to fix on a whiteboard than in production. Yet most WAF tooling assumes the architecture already exists — either because there are deployed resources to scan (Advisor) or because someone has built enough of it to answer 60 specific questions about it (WAR). That leaves a gap. Between "rough sketch" and "deployed resource group" there is no algorithmic WAF feedback loop. That's the gap the Diagram Builder fills. Microsoft's two official WAF assessment algorithms Before describing our approach, it's worth being precise about what Microsoft already ships, because the term "WAF assessment algorithm" can mean either of two very different things. 1. Azure Well-Architected Review (WAR) — questionnaire-based The Well-Architected Review is a free self-assessment hosted on Microsoft Learn. Aspect Detail Input Human answers to ~60 questions mapped to the WAF pillar checklists Workload variants Core WAR, plus AI/ML, IoT, SAP on Azure, Azure Stack Hub, SaaS, Mission Critical Scoring Derived from the answers — each "no" or unanswered question subtracts from the pillar score Output Per-pillar maturity score + prioritized recommendations + optional Advisor integration Improvement tracking "Milestones" (point-in-time snapshots) When to use Periodic deep reviews; greenfield design baselining; brownfield audits WAR is human-driven. The algorithm is essentially "how many of the recommended practices have you confirmed you do?" — which is exactly the right algorithm when the assessor is the workload team itself. 2. Azure Advisor Score — telemetry-based The Advisor score is the closest thing Microsoft ships to a real, deterministic WAF algorithm. It runs continuously over your deployed Azure resources. The math: Pillar-specific overrides: Security uses Microsoft Defender for Cloud's Secure Score model. Cost weights by retail $ cost of healthy resources, plus age-of-recommendation weighting; postponed/dismissed items are removed from the denominator. Reliability / Performance / Operational Excellence use the healthy-resources ratio above. Key terms: Healthy resource — a deployed resource with no open Advisor recommendation against it for that pillar. Total applicable — resources Advisor was able to evaluate (excludes dismissed/snoozed). Advisor is the right tool once you're in production. It cannot help you before deployment, because there is nothing to count as "healthy" or "applicable." The missing stage: design time Here's the lifecycle, with each tool's domain shaded: Design / Diagram — Diagram Builder validation runs here. Operate / Observe — Azure Advisor runs here continuously. Periodic Review — WAR runs here, typically quarterly or at major milestones. These three stages are sequential and complementary. Our app does not replace Advisor or WAR — it adds a feedback loop earlier in the lifecycle, where corrections are cheapest. How design-time validation works in the Azure Architecture Diagram Builder The validator is a two-phase hybrid pipeline: deterministic local rules first, then LLM refinement. The full source lives in three files: src/services/architectureValidator.ts — orchestrator and prompt src/services/wafPatternDetector.ts — topology + service rule engine src/data/wafRules.ts — the rule knowledge base Phase 1 — Deterministic rule pre-scan (~1 ms, no LLM) When you click Validate Architecture, the validator runs a fully client-side rule engine against the diagram's services, connections, and groups. There are two kinds of rules: Architecture-pattern rules These fire when a topology anti-pattern is detected: Pattern Detection trigger single-region No global LB (Traffic Manager / Front Door) with ≥3 services single-database Exactly one database service, no replication signal no-cache Compute + database present, no Redis/CDN no-monitoring No Azure Monitor / App Insights / Log Analytics no-identity No Microsoft Entra ID no-waf Public web tier without WAF / Front Door / App Gateway direct-db-access An edge from a frontend service directly into a database no-key-vault 4+ services and no Key Vault no-backup Database present, no Azure Backup / Recovery Services no-api-gateway 2+ compute services and no APIM / App Gateway / Front Door Service-specific rules Every service in the in the generated Azure Architecture diagram is matched against SERVICE_SPECIFIC_RULES by normalized type — App Service, Functions, AKS, Cosmos DB, SQL Database, Storage, Key Vault, and 22 more. The knowledge base at a glance Metric Count Total rules 73 Architecture-pattern rules 10 Service-specific rules 63 Distinct Azure services covered 29 Rules tagged Reliability 18 Rules tagged Security 34 Rules tagged Cost Optimization 5 Rules tagged Operational Excellence 7 Rules tagged Performance Efficiency 9 The preliminary score Each finding has a severity, and severity drives a fixed point deduction from a starting score of 100: Severity Deduction critical −12 high −7 medium −3 low −1 Result is floored at 10 (so even a deliberately bad architecture scores at least 10) and ceilinged at 95 (no findings ≠ perfect — there's always something the model might still catch). This is the deterministic baseline before the LLM ever sees the architecture, and it's what makes the pipeline reproducible. Phase 2 — LLM contextual refinement The pre-scan output, the topology, and the optional natural-language description are folded into a focused prompt sent to one of seven Azure OpenAI models (GPT-5.1 through 5.4, GPT-5.x Codex variants, DeepSeek V3.2 Speciale, Grok 4.1 Fast). The system prompt gives the model explicit scoring guardrails: Score based on what IS present, not what COULD be added. A well-connected architecture with appropriate services should score 60–80. Score below 50 only for critical gaps (no auth, no monitoring, single points of failure). Findings are improvement suggestions, not reasons to penalize the score severely. The model returns strict JSON: { "overallScore": 0-100, "summary": "2–3 sentence assessment", "pillars": [ { "pillar": "Reliability | Security | Cost Optimization | Operational Excellence | Performance Efficiency", "score": 0-100, "findings": [ { "severity": "critical | high | medium | low", "category": "...", "issue": "...", "recommendation": "...", "resources": ["service-name-1", "service-name-2"], "source": "rule-based | ai-analysis" } ] } ], "quickWins": [ /* same shape as findings */ ] } Two things to call out: Every finding is tagged rule-based or ai-analysis . That tag is the credibility lever. You can always see what the deterministic engine produced versus what the model contributed on top. If you don't trust the AI layer, you can ignore it entirely — the rule layer still stands. The LLM is given pattern hints, not the entire rule catalog. The prompt stays small and focused, which is roughly 3–5× faster and cheaper than asking the LLM to do everything from scratch. What the user sees On every run the modal reports: Overall WAF score (0–100) Per-pillar score × 5 (0–100 each) Severity breakdown — counts of critical / high / medium / low across all findings Quick wins — high-impact, low-effort items the model surfaces separately Hybrid metadata — local findings count, patterns detected, KB rules used, preliminary score, local elapsed ms AI metrics — model used, reasoning effort, prompt/completion/total tokens, elapsed time App Insights telemetry — an Architecture_Validated event with model, overall score, finding count, elapsed time Worked example Take this prompt, which I've used in demos with partners: "A multi-region web application: Azure Front Door in front of two App Service instances in West US 2 and East US 2, both reading from an Azure SQL Database with geo-replication, with Application Insights for telemetry. No Entra ID, no Key Vault." After generation, Validate Architecture runs: Phase 1 — pre-scan (deterministic), ~1 ms Patterns detected: no-identity , no-key-vault Findings produced: 8 (1 critical, 1 high, 3 medium, 3 low) Preliminary score: 100 − 12 − 7 − (3×3) − (1×3) = 69 Phase 2 — LLM refinement, ~6–9 s depending on model The model accepts the two pattern hints, validates them in context, and adds three more findings of its own: Finding Source Pillar Severity No Microsoft Entra ID for authentication rule-based Security critical No Key Vault for secret management rule-based Security high App Service slots not used for safe deploys ai-analysis Operational Excellence medium SQL DB geo-replication present but RTO/RPO not documented ai-analysis Reliability medium No CDN for static assets behind Front Door ai-analysis Performance Efficiency low Final scores returned by the model: Pillar Score Reliability 78 Security 52 Cost Optimization 80 Operational Excellence 70 Performance Efficiency 75 Overall 71 The Security score is the lowest because two of the highest-severity findings landed there — exactly what a human reviewer would flag first. Multi-model comparison Because the deterministic floor is identical across runs, the Validation Comparison view becomes a fair shootout of what each LLM adds on top of the same baseline. The same diagram is scored by all seven models, and the UI surfaces: Overall score per model Per-pillar score per model Severity-count deltas Number of ai-analysis findings each model contributed Quick wins each model identified This is genuinely useful for two reasons. First, it shows that LLM scores vary — typically by ±5–10 points on the same architecture — which is exactly why we publish the rule-based vs ai-analysis tag. Second, it lets architects pick the model whose review style matches their own. How we align with Microsoft's algorithms Alignment point What it means Same five pillars Identical names and scope to the official WAF Same source material Rules derived from WAF docs and Azure Architecture Center service guides Severity-graded findings Map conceptually to Advisor's high/medium/low impact recommendations Per-pillar + overall scoring Mirrors WAR/Advisor output shape, so the results feel familiar Where we deliberately differ — and why Concern Microsoft Diagram Builder Why we differ Needs deployed resources Advisor: yes No — works on a diagram We're a design-time tool; the architecture doesn't exist yet Needs human Q&A WAR: yes No — derived from the diagram One-click validation inside the design flow Healthy/Applicable ratio Advisor: yes No No resource-health signal exists pre-deployment Subcategory fixed weights Advisor: yes No explicit weights Severity is the de-facto weight (12/7/3/1) Defender Secure Score for Security Advisor: yes No Defender requires deployed resources Cost-weighted scoring Advisor: yes No (separate Cost Estimation feature) Cost is a separate pipeline in our app AI/LLM refinement Neither Yes Catches context-specific issues a static catalog misses, and explains findings in natural language Multi-model comparison Neither Yes Lets architects see scoring variance across models Honest limitations I'd rather you hear these from me than discover them in production: LLM scores drift. ±5–10 points across models on the same diagram is normal. Treat the score as directional, the findings as actionable. The rule-based tag is your anchor. No live telemetry. We can't know if your App Service is actually using availability zones — only that you have App Service in the diagram. Advisor will tell you the truth post-deployment. Generic ruleset. No specialized workload branches yet (AI/ML, IoT, SAP, SaaS). WAR has those. No milestone tracking. Each validation run is independent. Compare runs manually using the Validation Comparison view. Rule coverage is finite. 29 services and 73 rules is a strong start but not exhaustive — the LLM layer exists in part to compensate for that gap. How to use all three together A lifecycle that actually works: Design — Use the Diagram Builder to sketch the architecture and validate at design time. Iterate until the per-pillar scores look reasonable and the critical/high findings are addressed. Deploy — Generate Bicep from the diagram, deploy, and let Azure Advisor start scoring real resources. Operate — Use Azure Advisor continuously. Use Defender Secure Score for security posture. Periodic review — Run a Core WAR every quarter or at major milestones to capture the things only humans know (business context, tradeoffs, planned debt). None of these three replace the others. They cover different stages of the same loop. What's next A few things on the roadmap I'd love feedback on: Milestone tracking so design-time scores can be compared over time the way WAR milestones work. Workload-specific rulesets mirroring WAR's branches — starting with AI/ML. Direct Advisor handoff — once a diagram is deployed, surface the corresponding Advisor recommendations in the same UI to close the loop. Try it, fork it, tell me where it's wrong Live app: https://aka.ms/diagram-builder Source: github.com/Arturo-Quiroga-MSFT/azure-architecture-diagram-builder Useful references: Azure Well-Architected Framework pillars Azure Well-Architected Review tool Azure Advisor score — calculation Use Azure WAF assessments (Advisor) Complete an Azure Well-Architected Review assessment If you're a partner or customer architect who's already living in Advisor and WAR, I'd genuinely value your reaction — does the design-time stage feel like a real gap to you, or are you already covering it some other way? Open an issue on the repo or reply on LinkedIn. Posted on the Azure Architecture Blog · Comments and issues welcome on the repo.Introducing Grok 4.3 on Microsoft Foundry: Latest Generation Agentic Capabilities
Customers building advanced AI systems increasingly need models that can reason deeply, act autonomously, and integrate reliably into real‑world workflows—all without compromising on governance or cost efficiency. Grok 4.3, xAI’s latest flagship model, is now available in Microsoft Foundry, giving developers and enterprises access to latest agentic intelligence within a production‑ready environment designed for scale. With Grok 4.3 on Microsoft Foundry, customers can more easily experiment with, evaluate, and deploy a powerful new option for agent‑based and domain‑specific applications—while benefiting from the safety controls, monitoring, and operational tooling needed to move from prototype to production with confidence. About Grok 4.3 Grok 4.3 is xAI’s latest flagship model, designed to support agent-based and productivity-focused workflows across a wide range of professional scenarios. Based on information provided by xAI and independent research conducted by Artificial Analysis, Grok 4.3 demonstrates strong performance across multiple benchmarks, reflecting a favorable balance between model capability and reported benchmark cost. *Benchmark data and cost metrics are provided by xAI and independently analyzed by Artificial Analysis. Source: https://artificialanalysis.ai Improved agentic capabilities Grok 4.3 is purpose‑built for agentic systems, improving in tool calling, instruction following, and lower hallucination, as reported by xAI. Grok 4.3 also enables policy‑aware support agents with reliable tool use and consistent behavior across extended conversations. On Microsoft Foundry, Grok 4.3 supports up to a 200k token context window, enabling extended multi‑turn reasoning and agent workflows. Multi-modal and domain‑specific strengths Grok 4.3 delivers strong performance across a range of professional and technical domains: Multimodal analysis: Native understanding across text, images, diagrams, and mixed data sources, enabling synthesis of visual and textual information for complex reasoning tasks. Web development: Excels in full‑stack web development, producing clean, production‑ready code with minimal guidance. Legal reasoning: supports interpretation of contracts, case law, and regulatory documents. Finance agents: supports financial analysis, modeling, and human decisions Built‑In Native Capabilities Grok 4.3 includes powerful native capabilities that simplify real‑world application development: Web search and X search for real‑time context Python code execution for analysis and automation File search (RAG) for enterprise knowledge grounding Excel, PDF, and PowerPoint generation for end‑to‑end workflows Together, these capabilities allow Grok 4.3 to function as a powerful agentic productivity engine, not just a language mode. Why Grok 4.3 on Microsoft Foundry Bringing Grok 4.3 to Microsoft Foundry delivers value beyond raw model performance. When deployed through Foundry, Azure AI Content Safety is enabled by default, adding an additional layer of protection for enterprise use. Customers can review the Microsoft Foundry model card for detailed safety and usage considerations. Microsoft Foundry also provides tools to support our customers with their responsible AI efforts, including model cards during selection, configurable guardrails such as jailbreak detection and content filtering, pre‑deployment evaluations and red teaming, and post‑deployment monitoring and governance. These capabilities help customers maintain output quality and deploy Grok 4.3 responsibly at scale. Pricing Model Deployment Input/1M Tokens Output/1M Tokens Availability Grok 4.3 Global Standard $1.25 $2.50 Public Preview Getting started Grok 4.3 is now available in Microsoft Foundry. Explore the model details in the Foundry model catalog, evaluate it using your own datasets, and start building and deployment in minutes.
Now in Foundry: Tongyi-MAI Z-Image-Turbo, with FLUX.1-schnell and SDXL base 1.0
This week's Model Mondays edition pairs three models available through the Hugging Face collection in Microsoft Foundry: Tongyi-MAI's Z-Image-Turbo, a new designed for lower latency on a single GPU and native bilingual text rendering; Black Forest Labs' FLUX.1-schnell, a 12B rectified flow transformer distilled to 1–4 step inference and one of the most adopted open-weight image models since its 2024 release; and Stability AI's stable-diffusion-xl-base-1.0 (SDXL), a latent diffusion research model that can be used to generate and modify images based on text prompts. Models of the week Tongyi-MAI: Z-Image-Turbo Model Specs Parameters / size: 6B (BF16) Resolution: Up to 1024×1024 native Primary task: Text-to-image generation (English and Chinese) Why it's interesting (Spotlight) Scalable Single-Stream Diffusion Transformer (S3-DiT) architecture: Z-Image concatenates text tokens, visual semantic tokens, and image VAE tokens into a single unified input stream rather than running text and image through separate branches. This single-stream design can improve parameter efficiency relative to dual-stream DiT architectures at the same capacity. See the Z-Image technical report for details. 8-step inference at sub-second latency, fits in 16GB VRAM: Z-Image-Turbo is distilled with Decoupled Distribution Matching Distillation (Decoupled-DMD) and further refined with DMDR, a method that fuses DMD with reinforcement learning during post-training. The result is a model that runs 8 Number-of-Function-Evaluations (NFE) per image with no Classifier-Free Guidance (CFG)—which roughly halves the per-step compute compared to CFG-based inference. See the Decoupled-DMD and DMDR papers. Native bilingual text rendering and strong instruction adherence: Unlike most open-weight image models, which struggle with legible in-image text, Z-Image-Turbo renders complex English and Chinese text accurately which is useful for posters, signage, packaging mockups, and marketing creative. Try it Imagine you're a community programs coordinator at your city's parks department, planning a new summer event series — a "Cake Picnic in the Park" — designed to bring neighbors together over food in shared green space. The event is a few weeks out. You haven't booked bakery partners yet, so no actual cake exists, and you need marketing assets this week to start driving sign-ups: a hero image for the registration page, a flyer for community centers and libraries, social tiles for the city's channels. Use the prompt below and a photorealistic image, that can now be scaled to become additional assets like printed flyers or social images in minutes using image editing tools (or another model). Prompt: A round layered cake displayed on a white ceramic cake stand, topped with glossy fresh red cherries and smooth pastel pink buttercream frosting piped in delicate rosettes around the edge. One generous slice has been cleanly cut and removed from the front, revealing a perfect cross-section: four distinct horizontal layers alternating between soft pink sponge cake and fluffy white vanilla cream frosting. Professional bakery photography, soft natural window light from the left, shallow depth of field, marble countertop, warm and inviting atmosphere, photorealistic detail on the cake texture, cherry highlights, and frosting swirls. Black Forest Labs: FLUX.1-schnell Model Specs Parameters / size: 12B (rectified flow transformer) Resolution: Flexible up to 2 megapixels Primary task: Text-to-image generation Why it's interesting (Spotlight) Rectified flow transformer with adversarial distillation for 1–4 step inference: FLUX.1-schnell is the distilled, Apache 2.0 sibling of the FLUX.1 family. It uses a rectified flow formulation (a diffusion variant that learns straight-line probability paths between noise and data, reducing the number of solver steps needed) and is further compressed with latent adversarial diffusion distillation. The model generates high quality images in for latency-sensitive workloads. Permissive licensing for commercial use: Released under Apache 2.0, FLUX.1-schnell can be used for personal, scientific, and commercial purposes. This has driven broad adoption across product features that need an open, redistributable image backbone. Strong prompt adherence at its parameter range: At 12B parameters, FLUX.1-schnell sits between the SDXL family and frontier proprietary image models, and it remains a common reference point for evaluating open image generation prompt following—particularly for complex compositional prompts and longer captions—roughly two years after its initial release. Try it Hugging Face Spaces give developers the ability to experiment and try new models before deploying them. Test out a few prompts here: https://black-forest-labs-flux-1-schnell.hf.space then when you are ready, deploy the model in Microsoft Foundry. Stability AI: stable-diffusion-xl-base-1.0 stabilityai/stable-diffusion-xl-base-1.0 · Hugging Face Model Specs Parameters / size: 2.6B UNet (≈3.5B total with text encoders) Resolution: 1024×1024 native Primary task: Text-to-image generation Why it's interesting (Spotlight) Dual text encoder design and an ensemble-of-experts pipeline: SDXL uses two pretrained text encoders—OpenCLIP-ViT/G and CLIP-ViT/L—concatenated to capture both broad semantic alignment and finer-grained token-level cues. It can be run standalone or paired with the SDXL refiner in an ensemble-of-experts pipeline where the base model handles early denoising and the refiner specializes in the final steps. See the SDXL report for the original training and architecture details. CreativeML Open RAIL++-M licensing for managed deployments: SDXL is distributed under the CreativeML Open RAIL++-M license, which permits commercial use and downstream fine-tuning with documented use restrictions. Try it To go deeper on SDXL, take a look at Stability AI's generative-models GitHub repository, which implements the most popular diffusion frameworks for both training and inference and continues to expand with new capabilities like distillation. Getting started You can deploy open-source Hugging Face models directly in Microsoft Foundry in two ways. The first by browsing the Hugging Face collection in the Foundry model catalog and deploying to managed endpoints in just a few clicks. The second way is direct through the Hugging Face Hub, select any supported model and then choose "Deploy on Microsoft Foundry", which brings you straight into Azure. Learn how to discover models and deploy them using Microsoft Foundry documentation: Follow along the Model Mondays series and access the GitHub to stay up to date on the latest Read Hugging Face on Azure docs Learn about one-click deployments from the Hugging Face Hub on Microsoft Foundry Explore models in Microsoft FoundryGoverning Agent Sprawl: A Multi‑Region AI Agent Landing Zone on Azure (Reference Architecture)
It doesn’t take long for AI agents to get out of hand. In most enterprises, the first few agents are celebrated. A chatbot here. A document summarizer there. Then another team ships an agent that calls APIs. Someone else connects one to internal data. Within months, IT is staring at dozens—or hundreds—of autonomous systems running across subscriptions, regions, and tools. At that point, the questions stop being about model quality and start being uncomfortable operational ones: Who owns this agent? What data can it access? What happens if it misbehaves? Why did it just consume half our monthly token budget in a day? Developers can build an AI agent in minutes—the difficult part is understanding what agents are doing, how they perform, and whether they comply with organizational policy. Signals scatter across tools, context is lost, and governance becomes reactive. This reference architecture exists to solve that problem. It describes a multi‑region AI agent landing zone on Azure that treats agents as first‑class, governable workloads—provisioned automatically, constrained by policy, and observable from day one. The architectural principle: separate control from execution The design starts with a simple but non‑negotiable rule: Control plane concerns must be separated from runtime concerns. Azure landing zones already follow this model. Management groups, Azure Policy, and RBAC are global constructs. Workloads run in regions. This architecture applies the same discipline to AI agents. The runtime plane is where agents execute, models infer, and data flows—often in multiple Azure regions. The control plane is where identity, policy, safety, evaluation, and oversight live—independent of region. This separation is what allows teams to scale agents without losing control. Layer 1: Azure AI Gateway — governing every request The first control layer sits directly in the request path. The AI gateway in Azure API Management provides a policy‑enforcement and observability layer in front of AI models, agents, and tools. It is not a separate service—it extends Azure API Management. Everything flows through it: Microsoft Foundry model deployments Azure AI Model Inference API endpoints OpenAI‑compatible third‑party models Self‑hosted models MCP servers and A2A agent APIs (preview) What the gateway actually enforces This layer is intentionally narrow and operational: Token quotas and rate limits The llm-token-limit policy (GA) enforces tokens‑per‑minute or quota ceilings per consumer before requests reach the backend. This prevents one application—or one agent—from exhausting shared capacity. Content safety at ingress The llm-content-safety policy (GA) integrates Azure AI Content Safety to moderate prompts automatically. Unsafe requests never reach the model. Traffic routing and resiliency Azure API Management supports multi‑region gateway deployment (Premium tier). If a region fails, traffic routes to the next closest gateway automatically. Token usage, prompts, and completions are logged to Azure Monitor and Application Insights using built‑in policies such as llm-emit-token-metric. The gateway does not understand agent intent or business context. That is by design. It governs traffic, not behavior. Layer 2: Azure AI Foundry Control Plane — governing behavior at scale The second layer governs what agents do, not just how requests flow. Azure AI Foundry Control Plane provides a unified management surface for AI agents, models, and tools across projects and subscriptions. It is designed specifically for agentic systems. Foundry Control Plane is currently in public preview. What Foundry Control Plane adds Fleet‑wide inventory Every agent, model, and tool appears in a single, searchable view across projects. Continuous evaluation on production traffic Foundry runs evaluations that measure task adherence, groundedness, tool‑call accuracy, sensitive data exposure, and other agent‑specific risk dimensions. Centralized guardrails Policy is enforced across inputs, outputs, and tool interactions—not just prompts. Bulk remediation can be applied across the fleet. Security integration Foundry integrates with: Microsoft Entra for agent identity (Entra Agent ID) Microsoft Defender for threat signals Microsoft Purview for data protection and compliance visibility Foundry Control Plane also requires an AI Gateway to be configured for advanced governance scenarios—reinforcing the layered approach. Layer 3: Microsoft Agent 365 — enterprise oversight, not just Azure oversight The third layer exists because Azure governance alone is not enough. Agents don’t just call APIs. They act on behalf of users. They access enterprise data. They operate inside Microsoft 365 workflows. Microsoft Agent 365 is the tenant‑level control plane for AI agents. It brings agents under the same administrative model used for users and applications. Status: Frontier Preview General availability: May 1, 2026 Why this layer matters Agent 365 introduces controls that Azure alone cannot provide: Agent registry A single inventory of all agents in the tenant—including sanctioned and shadow agents. Unsanctioned agents can be quarantined. Identity‑first access control Every agent is issued an Entra agent ID. Conditional Access policies apply to agents the same way they do to users. Human‑in‑the‑loop oversight Agents surface in Microsoft 365 admin workflows, not just Azure portals. Security and compliance Defender and Purview extend threat detection and data protection policies to agent activity. Agent 365 does not replace Foundry Control Plane. It complements it—connecting agent operations to enterprise identity, compliance, and productivity systems. How the pieces work together Individually, these services are powerful. The architecture works because they are deliberately layered. External approval → automated provisioning When a use case is approved in an external governance system, it triggers an Azure DevOps pipeline using the REST API. That pipeline: Provisions subscriptions and resource groups Deploys Foundry projects Configures Azure API Management with AI Gateway policies Enables monitoring and logging Governance is applied before the first request is made. One policy model, many regions Azure landing zones are region‑agnostic at the governance layer. This architecture follows that guidance. Policies and RBAC apply globally AI Gateway enforces limits locally in each region Runtime services scale region by region Expanding to a new region does not introduce a new governance model—only new capacity. A single operational view Signals flow upward: AI Gateway emits traffic and usage metrics Foundry Control Plane correlates evaluations, guardrail enforcement, and security alerts Agent 365 aggregates tenant‑level identity, compliance, and threat signals Operations teams no longer hunt across dashboards. They work from one prioritized view, with context intact. What this architecture deliberately does not promise This is a reference architecture, not a silver bullet. It does not eliminate the need for: Clear agent ownership Business‑level approval processes Ongoing evaluation of agent usefulness What it does provide is a foundation—one that lets organizations scale agentic AI without accepting chaos as the cost of innovation. Closing thoughts Agent sprawl is not a tooling failure. It’s an architectural one. By separating control from execution, layering governance where it belongs, and aligning AI operations with existing Azure and Microsoft 365 control planes, this architecture gives enterprises a way to move fast without losing sight of what their agents are doing. That’s the difference between experimentation—and production. Co-Contributor: Jorge Pena Alarcon-Sr. Cloud & AI Specialist References (official Microsoft sources) Azure AI Gateway in Azure API Management Configure AI Gateway for Foundry Foundry Control Plane overview Microsoft Agent 365 announcement Agent 365 GA annoucement Azure landing zones and regions Azure DevOps pipeline REST APIA New Chapter for Realtime AI: Reasoning, Translation, and Real-Time Transcription
Voice can be one of the most direct and productive interfaces for AI — enabling customer support agents that may resolve issues without a single keystroke, live multilingual communication that can take on language barriers as conversations happen, and voice assistants capable of reasoning through complex requests in real time. Developers building these experiences need models that can keep pace with increasingly demanding latency, accuracy, and language coverage requirements. Today, OpenAI’s GPT-realtime-translate, GPT‑realtime‑2 and, GPT-realtime-whisper are rolling out into Microsoft Foundry starting today — together representing a significant step forward for the realtime model lineup available to developers on the platform. GPT-realtime-translate and GPT-realtime-whisper GPT-realtime-translate and GPT-realtime-whisper together extend the realtime stack for live multilingual audio workflows. GPT-realtime-translate is built for continuous, real-time translation, producing translated output as speech unfolds without relying on segmented pipeline processing, while GPT-realtime-whisper provides low-latency streaming transcription of the original audio in parallel. Used together, they help developers support scenarios such as live events, cross-language customer experiences, captions, monitoring, and archival workflows that require both translated output and visibility into the source speech. Continuous stream processing: This new model translates live audio without segmenting or buffering allowing for more natural interactions. New translation and transcription capabilities: Translate between languages in real time and observe faster text to speech. Available via the Realtime API GPT-realtime-2 GPT‑realtime‑2 is a generational upgrade to OpenAI's speech-to-speech model, bringing internal reasoning and an expanded context window to real-time voice applications. Where previous speech to speech models responded immediately, GPT‑realtime‑2 can work through a problem before speaking — making it well suited for voice applications that need to handle complex, multi-step queries entirely in the audio layer without routing to a separate text pipeline. Native reasoning capability: The newest realtime model introduces stronger reasoning capabilities. Now the model thinks internally before responding. Adjustable reasoning effort via {reasoning.effort}: Explicitly request the level of reasoning the model uses -- minimal, low, medium, high – to save on cost and latency. Audio in, audio out: No need for an intermediary text step, conversation stays fluid and natural. Available via the Realtime API This models is coming soon to Microsoft Foundry. Since, May 6, the models have been rolling out into the model catalog. We are excited for you to explore and build with our evolving collection of frontier models. Use cases These models work independently, but they're designed to complement each other in real-world pipelines: Live multilingual events. GPT-realtime-translate enables real-time translation of live audio, producing translated speech along with a transcript in the target language. GPT‑realtime‑whisper can be used in parallel to capture a transcription of the original speech for captions, monitoring, or archival purposes. Together, they enable multilingual live streaming with both translated experiences and visibility into the source language. Global customer support. Route inbound calls through GPT-realtime-translate to translate conversations in real time and provide a translated transcript for agents. Use GPT‑realtime‑whisper alongside it to capture the original conversation as text for compliance, quality review, or analytics. Then pass the interaction to an agent built with GPT‑realtime‑2 using {reasoning.effort}: high for complex issue resolution, all within a continuous audio pipeline. International voice assistants. Build once and deploy across languages. GPT-realtime-translate enables multilingual interaction and provides translated output with a target-language transcript, while GPT‑realtime‑whisper can optionally capture the original user input as text. GPT‑realtime‑2 manages reasoning and conversational context, supporting more complex voice interactions. Pricing Model Deployment Modality Pricing per 1M tokens Input Cached Input Output GPT-realtime-2 Global Standard Audio $32.00 $0.40 $64.00 Text $4.00 $0.40 $24.00 Image $5.00 $0.50 -- GPT-realtime-translate Global Standard Audio -- -- $2.04/hour GPT-realtime-whisper Global Standard Audio -- -- $1.02/hour *Pricing for GPT-realtime-translate and GPT-realtime-whisper will be done by the hour Getting Started Looking for ways to dive in? GPT-realtime-translate, GPT-realtime-whisper, and GPT‑realtime‑2 are rolling out into Microsoft Foundry today. Explore the model catalog and start building: https://ai.azure.comHow to Modernise a Microsoft Access Database (Forms + VBA) to Node.JS, OpenAPI and SQL Server
Microsoft Access has played a significant role in enterprise environments for over three decades. Released in November 1992, its flexibility and ease of use made it a popular choice for organizations of all sizes—from FTSE250 companies to startups and the public sector. The platform enables rapid development of graphical user interfaces (GUIs) paired with relational databases, allowing users to quickly create professional-looking applications. Developers, data architects, and power users have all leveraged Microsoft Access to address various enterprise challenges. Its integration with Microsoft Visual Basic for Applications (VBA), an object-based programming language, ensured that Access solutions often became central to business operations. Unsurprisingly, modernizing these applications is a common requirement in contemporary IT engagements as thse solutions lead to data fragmentation, lack of integration into master data systems, multiple copies of the same data replicated across each access database and so on. At first glance, upgrading a Microsoft Access application may seem simple, given its reliance on forms, VBA code, queries, and tables. However, substantial complexity often lurks beneath this straightforward exterior. Modernization efforts must consider whether to retain the familiar user interface to reduce staff retraining, how to accurately re-implement business logic, strategies for seamless data migration, and whether to introduce an API layer for data access. These factors can significantly increase the scope and effort required to deliver a modern equivalent, especially when dealing with numerous web forms, making manual rewrites a daunting task. This is where GitHub Copilot can have a transformative impact, dramatically reducing redevelopment time. By following a defined migration path, it is possible to deliver a modernized solution in as little as two weeks. In this blog post, I’ll walk you through each tier of the application and give you example prompts used at each stage. 🏛️Architecture Breakdown: The N-Tier Approach Breaking down the application architecture reveals a classic N-Tier structure, consisting of a presentation layer, business logic layer, data access layer, and data management layer. 💫First-Layer Migration: Migrating a Microsoft Access Database to SQL Server The migration process began with the database layer, which is typically the most straightforward to move from Access to another relational database management system (RDBMS). In this case, SQL Server was selected to leverage the SQL Server Migration Assistant (SSMA) for Microsoft Access—a free tool from Microsoft that streamlines database migration to SQL Server, Azure SQL Database, or Azure SQL Database Managed Instance (SQLMI). While GitHub Copilot could generate new database schemas and insert scripts, the availability of a specialized tool made the process more efficient. Using SSMA, the database was migrated to SQL Server with minimal effort. However, it is important to note that relationships in Microsoft Access may lack explicit names. In such cases, SSMA appends a GUID or uses one entirely to create unique foreign key names, which can result in confusing relationship names post-migration. Fortunately, GitHub Copilot can batch-rename these relationships in the generated SQL scripts, applying more meaningful naming conventions. By dropping and recreating the constraints, relationships become easier to understand and maintain. SSMA handles the bulk of the migration workload, allowing you to quickly obtain a fully functional SQL Server database containing all original data. In practice, renaming and recreating constraints often takes longer than the data migration itself. Prompt Used: # Context I want to refactor the #file:script.sql SQL script. Your task is to follow the below steps to analyse it and refactor it according to the specified rules. You are allowed to create / run any python scripts or terminal commands to assist in the analysis and refactoring process. # Analysis Phase Identify: Any warning comments Relations between tables Foreign key creation References to these foreign keys in 'MS_SSMA_SOURCE' metadata # Refactor Phase Refactor any SQL matching the following rules: - Create a new script file with the same name as the original but with a `.refactored.sql` extension - Rename any primary key constraints to follow the format PK_{table_name}_{column_name} - Rename any foreign key constraints like [TableName]${GUID} to FK_{child_table}_{parent_table} - Rename any indexes like [TableName]${GUID} to IDX_{table_name}_{column_name} - Ensure any updated foreign keys are updated elsewhere in the script - Identify which warnings flagged by the migration assistant need addressed # Summary Phase Create a summary file in markdown format with the following sections: - Summary of changes made - List of warnings addressed - List of foreign keys renamed - Any other relevant notes 🤖Bonus: Introduce Database Automation and Change Management As we now had a SQL database, we needed to consider how we would roll out changes to the database and we could introduce a formal tool to cater for this within the solution which was Liquibase. Prompt Used: # Context I want to refactor #file:db.changelog.xml. Your task is to follow the below steps to analyse it and refactor it according to the specified rules. You are allowed to create / run any python scripts or terminal commands to assist in the analysis and refactoring process. # Analysis Phase Analyse the generated changelog to identify the structure and content. Identify the tables, columns, data types, constraints, and relationships present in the database. Identify any default values, indexes, and foreign keys that need to be included in the changelog. Identify any vendor specific data types / fucntions that need to be converted to common Liquibase types. # Refactor Phase DO NOT modify the original #file:db.changelog.xml file in any way. Instead, create a new changelog file called `db.changelog-1-0.xml` to store the refactored changesets. The new file should follow the structure and conventions of Liquibase changelogs. You can fetch https://docs.liquibase.com/concepts/data-type-handling.html to get available Liquibase types and their mappings across RDBMS implementations. Copy the original changesets from the `db.changelog.xml` file into the new file Refactor the changesets according to the following rules: - The main changelog should only include child changelogs and not directly run migration operations - Child changelogs should follow the convention db.changelog-{version}.xml and start at 1-0 - Ensure data types are converted to common Liquibase data types. For example: - `nvarchar(max)` should be converted to `TEXT` - `datetime2` should be converted to `TIMESTAMP` - `bit` should be converted to `BOOLEAN` - Ensure any default values are retained but ensure that they are compatible with the liquibase data type for the column. - Use standard SQL functions like `CURRENT_TIMESTAMP` instead of vendor-specific functions. - Only use vendor specific data types or functions if they are necessary and cannot be converted to common Liquibase types. These must be documented in the changelog and summary. Ensure that the original changeset IDs are preserved for traceability. Ensure that the author of all changesets is "liquibase (generated)" # Validation Phase Validate the new changelog file against the original #file:db.changelog.xml to ensure that all changesets are correctly refactored and that the structure is maintained. Confirm no additional changesets are added that were not present in the original changelog. # Finalisation Phase Provide a summary of the changes made in the new changelog file. Document any vendor specific data types or functions that were used and why they could not be converted to common Liquibase types. Ensure the main changelog file (`db.changelog.xml`) is updated to include the new child changelog file (`db.changelog-1-0.xml`). 🤖Bonus: Synthetic Data Generation Since the legacy system lacked synthetic data for development or testing, GitHub Copilot was used to generate fake seed data. Care was taken to ensure all generated data was clearly fictional—using placeholders like ‘Fake Name’ and ‘Fake Town’—to avoid any confusion with real-world information. This step greatly improved the maintainability of the project, enabling developers to test features without handling sensitive or real data. 💫Second-Layer Migration: OpenAPI Specifications With data migration complete, the focus shifted to implementing an API-driven approach for data retrieval. Adopting modern standards, OpenAPI specifications were used to define new RESTful APIs for creating, reading, updating, and deleting data. Because these APIs mapped directly to underlying entities, GitHub Copilot efficiently generated the required endpoints and services in Node.js, utilizing a repository pattern. This approach not only provided robust APIs but also included comprehensive self-describing documentation, validation at the API boundary, automatic error handling, and safeguards against invalid data reaching business logic or database layers. 💫Third-Layer Migration: Business Logic The business logic, originally authored in VBA, was generally straightforward. GitHub Copilot translated this logic into its Node.js equivalent and created corresponding tests for each method. These tests were developed directly from the code, adding a layer of quality assurance that was absent in the original Access solution. The result was a set of domain services mirroring the functionality of their VBA predecessors, successfully completing the migration of the third layer. At this stage, the project had a new database, a fresh API tier, and updated business logic, all conforming to the latest organizational standards. The final major component was the user interface, an area where advances in GitHub Copilot’s capabilities became especially evident. 💫Fourth Layer: User Interface The modernization of the Access Forms user interface posed unique challenges. To minimize retraining requirements, the new system needed to retain as much of the original layout as possible, ensuring familiar placement of buttons, dropdowns, and other controls. At the same time, it was necessary to meet new accessibility standards and best practices. Some Access forms were complex, spanning multiple tabs and containing numerous controls. Manually describing each interface for redevelopment would have been time-consuming. Fortunately, newer versions of GitHub Copilot support image-based prompts, allowing screenshots of Access Forms to serve as context. Using these screenshots, Copilot generated Government Digital Service Views that closely mirrored the original application while incorporating required accessibility features, such as descriptive labels and field selectors. Although the automatically generated UI might not fully comply with all current accessibility standards, prompts referencing WCAG guidelines helped guide Copilot’s improvements. The generated interfaces provided a strong starting point for UX engineers to further refine accessibility and user experience to meet organizational requirements. 🤖Bonus: User Story Generation from the User Interface For organizations seeking a specification-driven development approach, GitHub Copilot can convert screenshots and business logic into user stories following the “As a … I want to … So that …” format. While not flawless, this capability is invaluable for systems lacking formal requirements, giving business analysts a foundation to build upon in future iterations. 🤖Bonus: Introducing MongoDB Towards the end of the modernization engagement, there was interest in demonstrating migration from SQL Server to MongoDB. GitHub Copilot can facilitate this migration, provided it is given adequate context. As with all NoSQL databases, the design should be based on application data access patterns—typically reading and writing related data together. Copilot’s ability to automate this process depends on a comprehensive understanding of the application’s data relationships and patterns. # Context The `<business_entity>` entity from the existing system needs to be added to the MongoDB schema. You have been provided with the following: - #file:documentation - System documentation to provide domain / business entity context - #file:db.changelog.xml - Liquibase changelog for SQL context - #file:mongo-erd.md - Contains the current Mongo schema Mermaid ERD. Create this if it does not exist. - #file:stories - Contains the user stories that will the system will be built around # Analysis Phase Analyse the available documentation and changelog to identify the structure, relationships, and business context of the `<business_entity>`. Identify: - All relevant data fields and attributes - Relationships with other entities - Any specific data types, constraints, or business rules Determine how this entity fits into the overall MongoDB schema: - Should it be a separate collection? - Should it be embedded in another document? - Should it be a reference to another collection for lookups or relationships? - Explore the benefit of denormalization for performance and business needs Consider the data access patterns and how this entity will be used in the application. # MongoDB Schema Design Using the analysis, suggest how the `<business_entity>` should be represented in MongoDB: - The name of the MongoDB collection that will represent this entity - List each field in the collection, its type, any constraints, and what it maps to in the original business context - For fields that are embedded, document the parent collection and how the fields are nested. Nested fields should follow the format `parentField->childField`. - For fields that are referenced, document the reference collection and how the lookup will be performed. - Provide any additional notes on indexing, performance considerations, or specific MongoDB features that should be used - Always use pascal case for collection names and camel case for field names # ERD Creation Create or update the Mermaid ERD in `mongo-erd.md` to include the results of your analysis. The ERD should reflect: - The new collection or embedded document structure - Any relationships with other collections/entities - The data types, constraints, and business rules that are relevant for MongoDB - Ensure the ERD is clear and follows best practices for MongoDB schema design Each entity in the ERD should have the following layout: **Entity Name**: The name of the MongoDB collection / schema **Fields**: A list of fields in the collection, including: - Field Name (in camel case) - Data Type (e.g., String, Number, Date, ObjectId) - Constraints (e.g. indexed, unique, not null, nullable) In this example, Liquibase was used as a changelog to supply the necessary context, detailing entities, columns, data types, and relationships. Based on this, Copilot could offer architectural recommendations for new document or collection types, including whether to embed documents or use separate collections with cache references for lookup data. Copilot can also generate an entity relationship diagram (ERD), allowing for review and validation before proceeding. From there, a new data access layer can be generated, configurable to switch between SQL Server and MongoDB as needed. While production environments typically standardize on a single database model, this demonstration showcased the speed and flexibility with which strategic architectural components can be introduced using GitHub Copilot. 👨💻Conclusion This modernization initiative demonstrated how strategic use of automation and best practices can transform legacy Microsoft Access solutions into scalable, maintainable architectures utilizing Node.js, SQL Server, MongoDB, and OpenAPI. By carefully planning each migration layer—from database and API specifications to business logic—the team preserved core functionality while introducing modern standards and enhanced capabilities. GitHub Copilot played a pivotal role, not only speeding up redevelopment but also improving code quality through automated documentation, test generation, and meaningful naming conventions. The result was a significant reduction in development time, with a robust, standards-compliant system delivered in just two weeks compared to an estimated six to eight months using traditional manual methods. This project serves as a blueprint for organizations seeking to modernize their Access-based applications, highlighting the efficiency gains and quality improvements that can be achieved by leveraging AI-powered tools and well-defined migration strategies. The approach ensures future scalability, easier maintenance, and alignment with contemporary enterprise requirements.Resource Guide: Making Physical AI Practical for Real‑World Industrial Operations
Microsoft’s adaptive cloud approach enables organizations to turn operational technology (OT) data into intelligent actions, autonomously, without requiring everything to live in the cloud by unifying cloud-to-edge management plane, data plane, and intelligence platform. At the center of this approach are key foundational technologies: Key Purpose Offering Direct-to-cloud device management + telemetry ingestion Azure IoT Hub Industrial connectivity + edge data plane Azure IoT Operations Unified analytics + real-time intelligence Microsoft Fabric On-device AI inferencing runtime Foundry Local Microsoft Azure IoT Gartner winner: Microsoft named a Leader in the 2025 Gartner® Magic Quadrant™ for Global Industrial IoT Platforms See it all come together Before diving into each component, watch this end-to-end demo showing how Azure IoT Operations, Azure IoT Hub, Microsoft Fabric, and Foundry Local work as one stack across the edge-to-cloud lifecycle - Making industrial AI practical for real-world operations with adaptive cloud. How these components work together Azure IoT Operations and Azure IoT Hub collect real-time data from operational assets and send semantically-ready, modeled data to Microsoft Fabric, where it's contextualized with enterprise data for downstream analytics. Microsoft Foundry extends to the edge through Foundry Local, so the same tooling used to deploy and manage AI models in the cloud applies to edge use cases. All of it integrates into Azure Resource Manager, bringing OT devices, assets, and edge AI models into the same management and security paradigm as every other Azure-managed resource. This blog walks through where to get started with each product capability: 1. Manage Cloud-Connected Devices and Telemetry with Azure IoT Hub Azure IoT Hub is a fully managed cloud service that enables secure bidirectional communication, device-to-cloud telemetry ingestion, cloud-to-device command execution, per-device authentication, remote management and more. Telemetry from IoT Hub can also be routed downstream into analytics platforms like Microsoft Fabric for visualization or AI modeling. Recommended Usage: Devices that utilize IoT Hub are distributed, stand-alone devices with fixed-functions. These devices typically do not require cloud-managed containerized workloads or cloud-managed proximal industrial protocol connectivity. Examples of appropriate device-to-cloud IoT Hub endpoint devices include water monitoring stations, vehicle telematics, distributed fluid level sensors, etc. Resources Current in-market services overview: IoT Hub: What is Azure IoT Hub? - Azure IoT Hub DPS: Overview of Azure IoT Hub Device Provisioning Service - Azure IoT Hub Device Provisioning Service ADU: Introduction to Device Update for Azure IoT Hub Building scalable solutions with Azure IoT platform: Best practices for large-scale IoT deployments - Azure IoT Hub Device Provisioning Service Scale Out an Azure IoT Hub-based Solution to Support Millions of Devices - Azure Architecture Center Azure IoT Hub scaling Try out our preview of new IoT Hub capabilities (integration with Azure Device Registry and Certificate Management) Learn more about these capabilities on our blog post: Azure IoT Hub + Azure Device Registry (Preview Refresh): Device Trust and Management at Fleet Scale… Integration with Azure Device Registry (preview): Integration with Azure Device Registry (preview) - Azure IoT Hub Microsoft-backed X.509 certificate management (preview): What is Microsoft-backed X.509 Certificate Management (Preview)? - Azure IoT Hub How to start with the preview: Deploy IoT Hub with ADR integration and certificate management (Preview) - Azure IoT Hub 2. Connect Industrial Assets with Azure IoT Operations Azure IoT Operations provides a unified data plane for the edge that runs on Azure Arc–enabled Kubernetes clusters and supports open industrial standards. It allows organizations to connect and capture equipment telemetry, normalize OT data locally, route hot-path signals to real-time analytics, securely manage layered industrial networks, and more. Edge‑processed data can then be sent upstream to Microsoft Fabric for AI‑driven analysis. Recommended Usage: Azure IoT Operations is intended to be the data plane for an adaptive cloud deployment extending the management, data, and AI capabilities of the Microsoft cloud to an on-prem device. This device binds to these cloud planes providing a platform for local data processing and intermittent connectivity. The target for these devices range from a small-gateway-style PC to a full data center. Azure IoT Operations endpoints enable cloud-managed containerized workloads and cloud-managed proximal industrial protocol connectivity. Examples of appropriate adaptive cloud and Azure IoT Operations endpoints include, on-robot computers, industrial machine controllers, retail store sensor/vision processing, and top-of-factory site infrastructure for line of business applications. Resources Azure IoT Operations Overview Azure IoT Operations Documentation Hub Quickstart: explore-iot-operations/quickstart at main · Azure-Samples/explore-iot-operations Open-source framework for scaling robotics from simulation to production on Azure + NVIDIA: microsoft/physical-ai-toolchain Demo video showcasing How we built the demo: explore-iot-operations/quickstart at main · Azure-Samples/explore-iot-operations Edge-AI: microsoft/edge-ai: Production-ready Infrastructure as Code, applications, pluggable components, and… Latest Announcements & Blogs Making Physical AI Practical for Real-World Industrial Operations: Part 1 | Microsoft Community Hub Making Physical AI Practical for Real-World Industrial Operations: Part 2 | Microsoft Community Hub Unlock Industrial Intelligence | Microsoft Hannover Messe 2026 From pilots to production: How Microsoft and partners are accelerating intelligent operations 3. Advanced Analytics with Microsoft Fabric Microsoft Fabric delivers a unified, end‑to‑end analytics platform that transforms streaming OT telemetry into real‑time insights and live dashboards. Fabric Operations Agents monitor industrial signals to recommend targeted actions, while Fabric IQ provides a shared semantic foundation that enables AI agents to reason over enterprise data with business context. Together, Fabric turns live industrial data into AI‑powered operational intelligence. Resources Get Started with Microsoft Fabric Learning Path Fabric Real-Time Intelligence documentation - Microsoft Fabric | Microsoft Learn Create and Configure Operations Agents - Microsoft Fabric | Microsoft Learn Fabric IQ documentation - Microsoft Fabric | Microsoft Learn 4.Run AI Models On‑Device with Foundry Local Foundry Local extends on‑device AI to Arc‑enabled Kubernetes edge clusters, providing a Microsoft‑validated inferencing layer for running AI models in industrial, disconnected or sovereign environments. Resources Foundry Local on Azure Local Documentation Participate in Foundry Local on Azure Local preview form Foundry Local on Azure Local: HELM deployment Demo Customer Stories Chevron: Chevron plans facilities of the future with Azure IoT Operations Husqvarna: Husqvarna Group Boosts Operational Efficiency with Azure Adaptive Cloud Ecopetrol: Azure IoT Operations and Azure IoT for energy help Ecopetrol optimize energy distribution while lowering operational costs P&G: Procter & Gamble cuts model deployment time up to 90% with Azure IoT Operations Toyota: Toyota Industries innovates its paint shop processes with Azure industrial AI and Azure IoT Hub
