APAC Fabric Engineering Connection
๐ Upcoming Fabric Engineering Connection Call โ Americas & EMEA & APAC! Join us on Wednesday, January 14, 8โ9 am PT (Americas & EMEA) and Thursday, January 15, 1โ2 am UTC (APAC) for a special session featuring the latest Power BI Updates & Announcements from Ignite with Sujata Narayana, Rui Romano, and other members of the Power BI Product Team. Plus, hear from Tom Peplow on Developing Apps on OneLake APIs. ๐ To participate, make sure youโre a member of the Fabric Partner Community Teams Channel. If you havenโt joined yet, sign up here: https://lnkd.in/g_PRdfjt Donโt miss this opportunity to learn, connect, and stay up to date with the latest in Microsoft Fabric and Power BI!Americas & EMEA Fabric Engineering Connection
๐ Upcoming Fabric Engineering Connection Call โ Americas & EMEA & APAC! Join us on Wednesday, January 14, 8โ9 am PT (Americas & EMEA) and Thursday, January 15, 1โ2 am UTC (APAC) for a special session featuring the latest Power BI Updates & Announcements from Ignite with Sujata Narayana, Rui Romano, and other members of the Power BI Product Team. Plus, hear from Tom Peplow on Developing Apps on OneLake APIs. ๐ To participate, make sure youโre a member of the Fabric Partner Community Teams Channel. If you havenโt joined yet, sign up here: https://lnkd.in/g_PRdfjt Donโt miss this opportunity to learn, connect, and stay up to date with the latest in Microsoft Fabric and Power BI!AI Didnโt Break Your Production โ Your Architecture Did
Most AI systems donโt fail in the lab. They fail the moment production touches them. Iโm Hazem Ali โ Microsoft AI MVP, Principal AI & ML Engineer / Architect, and Founder & CEO of Skytells. With a strong foundation in AI and deep learning from low-level fundamentals to production-scale, backed by rigorous cybersecurity and software engineering expertise, I design and deliver enterprise AI systems end-to-end. I often speak about what happens after the pilot goes live: real users arrive, data drifts, security constraints tighten, and incidents force your architecture to prove it can survive. My focus is building production AI with a security-first mindset: identity boundaries, enforceable governance, incident-ready operations, and reliability at scale. My mission is simple: Architect and engineer secure AI systems that operate safely, predictably, and at scale in production. And hereโs the hard truth: AI initiatives rarely fail because the model is weak. They fail because the surrounding architecture was never engineered for production reality. - Hazem Ali You see this clearly when teams bolt AI onto an existing platform. In Azure-based environments, the foundation can be solidโidentity, networking, governance, logging, policy enforcement, and scale primitives. But that doesnโt make the AI layer production-grade by default. It becomes production-grade only when the AI runtime is engineered like a first-class subsystem with explicit boundaries, control points, and designed failure behavior. A quick moment from the field I still remember one rollout that looked perfect on paper. Latency was fine. Error rate was low. Dashboards were green. Everyone was relaxed. Then a single workflow started creating the wrong tickets, not failing or crashing. It was confidently doing the wrong thing at scale. It took hours before anyone noticed, because nothing was broken in the traditional sense. When we finally traced it, the model was not the root cause. The system had no real gates, no replayable trail, and tool execution was too permissive. The architecture made it easy for a small mistake to become a widespread mess. That is the gap Iโm talking about in this article. Production Failure Taxonomy This is the part most teams skip because it is not exciting, and it is not easy to measure in a demo. When AI fails in production, the postmortem rarely says the model was bad. It almost always points to missing boundaries, over-privileged execution, or decisions nobody can trace. So if your AI can take actions, you are no longer shipping a chat feature. You are operating a runtime that can change state across real systems, that means reliability is not just uptime. It is the ability to limit blast radius, reproduce decisions, and stop or degrade safely when uncertainty or risk spikes. You can usually tell early whether an AI initiative will survive production. Not because the model is weak, but because the failure mode is already baked into the architecture. Here are the ones I see most often. 1. Healthy systems that are confidently wrong Uptime looks perfect. Latency is fine. And the output is wrong. This is dangerous because nothing alerts until real damage shows up. 2. The agent ends up with more authority than the user The user asks a question. The agent has tools and credentials. Now it can do things the user never should have been able to do in that moment. 3. Each action is allowed, but the chain is not Read data, create ticket, send message. All approved individually. Put together, it becomes a capability nobody reviewed. 4. Retrieval becomes the attack path Most teams worry about prompt injection. Fair. But a poisoned or stale retrieval layer can be worse, because it feeds the model the wrong truth. 5. Tool calls turn mistakes into incidents The moment AI can change stateโconfig, permissions, emails, payments, or dataโa mistake is no longer a bad answer. It is an incident. 6. Retries duplicate side effects Timeouts happen. Retries happen. If your tool calls are not safe to repeat, you will create duplicate tickets, refunds, emails, or deletes. Next, letโs talk about what changes when you inject probabilistic behavior into a deterministic platform. In the Field: Building and Sharing Real-World AI In December 2025, I had the chance to speak and engage with builders across multiple AI and technology events, sharing what I consider the most valuable part of the journey: the engineering details that show up when AI meets production reality. This photo captures one of those moments: real conversations with engineers, architects, and decision-makers about what it truly takes to ship production-grade AI. During my session, Designing Scalable and Secure Architecture at the Enterprise Scale I walked through the ideas in this article live on stage then went deeper into the engineering reality behind them: from zero-trust boundaries and runtime policy enforcement to observability, traceability, and safe failure design, The goal wasnโt to talk about โAI capability,โ but to show how to build AI systems that operate safely and predictably at scale in production. Deterministic platforms, probabilistic behavior Most production platforms are built for deterministic behavior: defined contracts, predictable services, stable outputs. AI changes the physics. You introduce probabilistic behavior into deterministic pipelines and your failure modes multiply. An AI system can be confidently wrong while still looking โhealthyโ through basic uptime dashboards. Thatโs why reliability in production AI is rarely about โbetter promptsโ or โhigher model accuracy.โ Itโs about engineering the right control points: identity boundaries, governance enforcement, behavioral observability, and safe degradation. In other words: the model is only one component. The system is the product. Production AI Control Plane Hereโs the thing. Once you inject probabilistic behavior into a deterministic platform, you need more than prompts and endpoints. You need a control plane. Not a fancy framework. Just a clear place in the runtime where decisions get bounded, actions get authorized, and behavior becomes explainable when something goes wrong. This is the simplest shape I have seen work in real enterprise systems. The control plane components Orchestrator Owns the workflow. Decides what happens next, and when the system should stop. Retrieval Brings in context, but only from sources you trust and can explain later. Prompt assembly Builds the final input to the model, including constraints, policy signals, and tool schemas. Model call Generates the plan or the response. It should never be trusted to execute directly. Policy Enforcement Point The gate before any high impact step. It answers: is this allowed, under these conditions, with these constraints. Tool Gateway The firewall for actions. Scopes every operation, validates inputs, rate-limits, and blocks unsafe calls. Audit log and trace store A replayable chain for every request. If you cannot replay it, you cannot debug it. Risk engine Detects prompt injection signals, anomalous sessions, uncertainty spikes, and switches the runtime into safer modes. Approval flow For the few actions that should never be automatic. It is the line between assistance and damage. If you take one idea from this section, let it be this. The model is not where you enforce safety. Safety lives in the control plane. Next, letโs talk about the most common mistake teams make right after they build the happy-path pipeline. Treating AI like a feature. The common architectural trap: treating AI like a feature Many teams ship AI like a feature: prompt โ model โ response. That structure demos well. In production, it collapses the moment AI output influences anything stateful tickets, approvals, customer messaging, remediation actions, or security decisions. At that point, youโre not โadding AI.โ Youโre operating a semi-autonomous runtime. The engineering questions become non-negotiable: Can we explain why the system responded this way? Can we bound what itโs allowed to do? Can we contain impact when itโs wrong? Can we recover without human panic? If those answers arenโt designed into the architecture, production becomes a roulette wheel. Governance is not a document Itโs a runtime enforcement capability Most governance programs fail because theyโre implemented as late-stage checklists. In production, governance must live inside the execution path as an enforceable mechanism, A Policy Enforcement Point (PEP) that evaluates every high-impact step before it happens. At the moment of execution, your runtime must answer a strict chain of authorization questions: 1. What tools is this agent attempting to call? Every tool invocation is a privilege boundary. Your runtime must identify the tool, the operation, and the intended side effect (read vs write, safe vs state-changing). 2. Does the tool have the right permissions to run for this agent? Even before user context, the tool itself must be runnable by the agentโs workload identity (service principal / managed identity / workload credentials). If the agent identity canโt execute the tool, the call is denied period. 3. If the tool can run, is the agent permitted to use it for this user? This is the missing piece in most systems: delegation. The agent might be able to run the tool in general, but not on behalf of this user, in this tenant, in this environment, for this task category. This is where you enforce: user role / entitlement tenant boundaries environment (prod vs staging) session risk level (normal vs suspicious) 4. If yes, which tasks/operations are permitted? Tools are too broad. Permissions must be operation-scoped. Not โJira tool allowed.โ But โJira: create ticket only, no delete, no project-admin actions.โ Not โDatabase tool allowed.โ But โDB: read-only, specific schema, specific columns, row-level filters.โ This is ABAC/RBAC + capability-based execution. 5. What data scope is allowed? Even a permitted tool operation must be constrained by data classification and scope: public vs internal vs confidential vs PII row/column filters time-bounded access purpose limitation (โonly for incident triageโ) If the system canโt express data scope at runtime, it canโt claim governance. 6. What operations require human approval? Some actions are inherently high risk: payments/refunds changing production configs emailing customers deleting data executing scripts The policy should return โREQUIRE_APPROVALโ with clear obligations (what must be reviewed, what evidence is required, who can approve). 7. What actions are forbidden under certain risk conditions? Risk-aware policy is the difference between governance and theater. Examples: If prompt injection signals are high โ disable tool execution If session is anomalous โ downgrade to read-only mode If data is PII + user not entitled โ deny and redact If environment is prod + request is destructive โ block regardless of model confidence The key engineering takeaway Governance works only when itโs enforceable, runtime-evaluated, and capability-scoped: Agent identity answers: โCan it run at all?โ Delegation answers: โCan it run for this user?โ Capabilities answer: โWhich operations exactly?โ Data scope answers: โHow much and what kind of data?โ Risk gates + approvals answer: โWhen must it stop or escalate?โ If policy canโt be enforced at runtime, it isnโt governance. Itโs optimism. Safe Execution Patterns Policy answers whether something is allowed. Safe execution answers what happens when things get messy. Because they will, Models time out, Retries happen, Inputs are adversarial. People ask for the wrong thing. Agents misunderstand. And when tools can change state, small mistakes turn into real incidents. These patterns are what keep the system stable when the world is not. ๐ Two-phase execution Do not execute directly from a model output. First phase: propose a plan and a dry-run summary of what will change. Second phase: execute only after policy gates pass, and approval is collected if required. Idempotency for every write If a tool call can create, refund, email, delete, or deploy, it must be safe to retry. Every write gets an idempotency key, and the gateway rejects duplicates. This one change prevents a huge class of production pain. Default to read-only when risk rises When injection signals spike, when the session looks anomalous, when retrieval looks suspicious, the system should not keep acting. It should downgrade. Retrieve, explain, and ask. No tool execution. Scope permissions to operations, not tools Tools are too broad. Do not allow Jira. Allow create ticket in these projects, with these fields. Do not allow database access. Allow read-only on this schema, with row and column filters. Rate limits and blast radius caps Agents should have a hard ceiling. Max tool calls per request. Max writes per session. Max affected entities. If the cap is hit, stop and escalate. A kill switch that actually works You need a way to disable tool execution across the fleet in one move. When an incident happens, you do not want to redeploy code. You want to stop the bleeding. If you build these in early, you stop relying on luck. You make failure boring, contained, and recoverable. Think for scale, in the Era of AI for AI I want to zoom out for a second, because this is the shift most teams still design around. We are not just adding AI to a product. We are entering a phase where parts of the system can maintain and improve themselves. Not in a magical way. In a practical, engineering way. A self-improving system is one that can watch what is happening in production, spot a class of problems, propose changes, test them, and ship them safely, while leaving a clear trail behind it. It can improve code paths, adjust prompts, refine retrieval rules, update tests, and tighten policies. Over time, the system becomes less dependent on hero debugging at 2 a.m. What makes this real is the loop, not the model. Signals come in from logs, traces, incidents, drift metrics, and quality checks. The system turns those signals into a scoped plan. Then it passes through gates: policy and permissions, safe scope, testing, and controlled rollout. If something looks wrong, it stops, downgrades to read-only, or asks for approval. This is why scale changes. In the old world, scale meant more users and more traffic. In the AI for AI world, scale also means more autonomy. One request can trigger many tool calls. One workflow can spawn sub-agents. One bad signal can cause retries and cascades. So the question is not only can your system handle load. The question is can your system handle multiplication without losing control. If you want self-improving behavior, you need three things to be true: The system is allowed to change only what it can prove is safe to change. Every change is testable and reversible. Every action is traceable, so you can replay why it happened. When those conditions exist, self-improvement becomes an advantage. When they do not, self-improvement becomes automated risk. And this leads straight into governance, because in this era governance is not a document. It is the gate that decides what the system is allowed to improve, and under which conditions. Observability: uptime isnโt enough โ you need traceability and causality Traditional observability answers: Is the service up. Is it fast. Is it erroring. That is table stakes. Production AI needs a deeper truth: why did it do that. Because the system can look perfectly healthy while still making the wrong decision. Latency is fine. Error rate is fine. Dashboards are green. And the output is still harmful. To debug that kind of failure, you need causality you can replay and audit: Input โ context retrieval โ prompt assembly โ model response โ tool invocation โ final outcome Without this chain, incident response becomes guesswork. People argue about prompts, blame the model, and ship small patches that do not address the real cause. Then the same issue comes back under a different prompt, a different document, or a slightly different user context. The practical goal is simple. Every high-impact action should have a story you can reconstruct later. What did the system see. What did it pull. What did it decide. What did it touch. And which policy allowed it. When you have that, you stop chasing symptoms. You can fix the actual failure point, and you can detect drift before users do. RAG Governance and Data Provenance Most teams treat retrieval as a quality feature. In production, retrieval is a security boundary. Because the moment a document enters the context window, it becomes part of the systemโs brain for that request. If retrieval pulls the wrong thing, the model can behave perfectly and still lead you to a bad outcome. I learned this the hard way, I have seen systems where the model was not the problem at all. The problem was a single stale runbook that looked official, ranked high, and quietly took over the decision. Everything downstream was clean. The agent followed instructions, called the right tools, and still caused damage because the truth it was given was wrong. I keep repeating one line in reviews, and I mean it every time: Retrieval is where truth enters the system. If you do not control that, you are not governing anything. - Hazem Ali So what makes retrieval safe enough for enterprise use? Provenance on every chunk Every retrieved snippet needs a label you can defend later: source, owner, timestamp, and classification. If you cannot answer where it came from, you cannot trust it for actions. Staleness budgets Old truth is a real risk. A runbook from last quarter can be more dangerous than no runbook at all. If content is older than a threshold, the system should say it is old, and either confirm or downgrade to read-only. No silent reliance. Allowlisted sources per task Not all sources are valid for all jobs. Incident response might allow internal runbooks. Customer messaging might require approved templates only. Make this explicit. Retrieval should not behave like a free-for-all search engine. Scope and redaction before the model sees it Row and column limits, PII filtering, secret stripping, tenant boundaries. Do it before prompt assembly, not after the model has already seen the data. Citation requirement for high-impact steps If the system is about to take a high-impact action, it should be able to point to the sources that justified it. If it cannot, it should stop and ask. That one rule prevents a lot of confident nonsense. Monitor retrieval like a production dependency Track which sources are being used, which ones cause incidents, and where drift is coming from. Retrieval quality is not static. Content changes. Permissions change. Rankings shift. Behavior follows. When you treat retrieval as governance, the system stops absorbing random truth. It consumes controlled truth, with ownership, freshness, and scope. That is what production needs. Security: API keys arenโt a strategy when agents can act The highest-impact AI incidents are usually not model hacks. They are architectural failures: over-privileged identities, blurred trust boundaries, unbounded tool access, and unsafe retrieval paths. Once an agent can call tools that mutate state, treat it like a privileged service, not a chatbot. Least privilege by default Explicit authorization boundaries Auditable actions Containment-first design Clear separation between user intent and system authority This is how you prevent a prompt injection from turning into a system-level breach. If you want the deeper blueprint and the concrete patterns for securing agents in practice, I wrote a full breakdown here: Zero-Trust Agent Architecture: How to Actually Secure Your Agents What โproduction-ready AIโ actually means Production-ready AI is not defined by a benchmark score. Itโs defined by survivability under uncertainty. A production-grade AI system can: Explain itself with traceability. Enforce policy at runtime. Contain blast radius when wrong. Degrade safely under uncertainty. Recover with clear operational playbooks. If your system canโt answer โhow does it fail?โ you donโt have production AI yet.. You have a prototype with unmanaged risk. How Azure helps you engineer production-grade AI Azure doesnโt โsolveโ production-ready AI by itself, it gives you the primitives to engineer it correctly. The difference between a prototype and a survivable system is whether you translate those primitives into runtime control points: identity, policy enforcement, telemetry, and containment. 1. Identity-first execution (kill credential sprawl, shrink blast radius) A production AI runtime should not run on shared API keys or long-lived secrets. In Azure environments, the most important mindset shift is: every agent/workflow must have an identity and that identity must be scoped. Guidance Give each agent/orchestrator a dedicated identity (least privilege by default). Separate identities by environment (prod vs staging) and by capability (read vs write). Treat tool invocation as a privileged service call, never โjust a function.โ Why this matters If an agent is compromised (or tricked via prompt injection), identity boundaries decide whether it can read one table or take down a whole environment. 2. Policy as enforcement (move governance into the execution path) Your articleโs core idea governance is runtime enforcement maps perfectly to Azureโs broader governance philosophy: policies must be enforceable, not advisory. Guidance Create an explicit Policy Enforcement Point (PEP) in your agent runtime. Make the PEP decision mandatory before executing any tool call or data access. Use โallow + obligationsโ patterns: allow only with constraints (redaction, read-only mode, rate limits, approval gates, extra logging). Why this matters Governance fails when itโs a document. It works when itโs compiled into runtime decisions. 3. Observability that explains behavior Azureโs telemetry stack is valuable because itโs designed for distributed systems: correlation, tracing, and unified logs. Production AI needs the same plus decision traceability. Guidance Emit a trace for every request across: retrieval โ prompt assembly โ model call โ tool calls โ outcome. Log policy decisions (allow/deny/require approval) with policy version + obligations applied. Capture โwhyโ signals: risk score, classifier outputs, injection signals, uncertainty indicators. Why this matters When incidents happen, you donโt just debug latency โ you debug behavior. Without causality, you canโt root-cause drift or containment failures. 4. Zero-trust boundaries for tools and data Azure environments tend to be strong at network segmentation and access control. That foundation is exactly what AI systems need because AI introduces adversarial inputs by default. Guidance Put a Tool Gateway in front of tools (Jira, email, payments, infra) and enforce scopes there. Restrict data access by classification (PII/secret zones) and enforce row/column constraints. Degrade safely: if risk is high, drop to read-only, disable tools, or require approval. Why this matters Prompt injection doesnโt become catastrophic when your system has hard boundaries and graceful failure modes. 5. Practical โproduction-readyโ checklist (Azure-aligned, engineering-first) If you want a concrete way to apply this: Identity: every runtime has a scoped identity; no shared secrets PEP: every tool/data action is gated by policy, with obligations Traceability: full chain captured and correlated end-to-end Containment: safe degradation + approval gates for high-risk actions Auditability: policy versions and decision logs are immutable and replayable Environment separation: prod โ staging identities, tools, and permissions Outcome This is how you turn โwe integrated AIโ into โwe operate AI safely at scale.โ Operating Production AI A lot of teams build the architecture and still struggle, because production is not a diagram. It is a living system. So here is the operating model I look for when I want to trust an AI runtime in production. The few SLOs that actually matter Trace completeness For high-impact requests, can we reconstruct the full chain every time, without missing steps. Policy coverage What percentage of tool calls and sensitive reads pass through the policy gate, with a recorded decision. Action correctness Not model accuracy. Real-world correctness. Did the system take the right action, on the right target, with the right scope. Time to contain When something goes wrong, how fast can we stop tool execution, downgrade to read-only, or isolate a capability. Drift detection time How quickly do we notice behavioral drift before users do. The runbooks you must have If you operate agents, you need simple playbooks for predictable bad days: Injection spike โ safe mode, block tool execution, force approvals Retrieval poisoning suspicion โ restrict sources, raise freshness requirements, require citations Retry storm โ enforce idempotency, rate limits, and circuit breakers Tool gateway instability โ fail closed for writes, degrade safely for reads Model outage โ fall back to deterministic paths, templates, or human escalation Clear ownership Someone has to own the runtime, not just the prompts. Platform owns the gates, tool gateway, audit, and tracing Product owns workflows and user-facing behavior Security owns policy rules, high-risk approvals, and incident procedures When these pieces are real, production becomes manageable. When they are not, you rely on luck and hero debugging. The 60-second production readiness checklist If you want a fast sanity check, here it is. Every agent has an identity, scoped per environment No shared API keys for privileged actions Every tool call goes through a policy gate with a logged decision Permissions are scoped to operations, not whole tools Writes are idempotent, retries cannot duplicate side effects Tool gateway validates inputs, scopes data, and rate-limits actions There is a safe mode that disables tools under risk There is a kill switch that stops tool execution across the fleet Retrieval is allowlisted, provenance-tagged, and freshness-aware High-impact actions require citations or they stop and ask Audit logs are immutable enough to trust later Traces are replayable end-to-end for any incident If most of these are missing, you do not have production AI yet. You have a prototype with unmanaged risk. A quick note In Azure-based enterprises, you already have strong primitives that mirror the mindset production AI requires: identity-first access control (Microsoft Entra ID), secure workload authentication patterns (managed identities), and deep telemetry foundations (Azure Monitor / Application Insights). The key is translating that discipline into the AI runtime so governance, identity, and observability arenโt external add-ons, but part of how AI executes and acts. Closing Models will keep evolving. Tooling will keep improving. But enterprise AI success still comes down to systems engineering. If youโre building production AI today, what has been the hardest part in your environment: governance, observability, security boundaries, or operational reliability? If youโre dealing with deep technical challenges around production AI, agent security, RAG governance, or operational reliability, feel free to connect with me on LinkedIn. Iโm open to technical discussions and architecture reviews. Thanks for reading. โ Hazem AliNew Microsoft Certified: AI Transformation Leader Certification
Are you a leader who is ready to transform your business with AI? Do you choose the right AI tools, plan AI adoption, streamline processes, and innovate with Microsoft 365 Copilot and Azure AI services? Can you identify the value of generative AI, along with the benefits and capabilities of Microsoftโs AI apps and services? If this is your skill set, we have a new Microsoft Certification for you. The Microsoft Certified: AI Transformation Leader Certification validates your expertise in these skills. To earn this Certification, you need to pass Exam AB-731: AI Transformation Leader, currently in beta. The new Certification shows employers that you understand the principles of responsible AI and governance, so your teams can innovate safely and ethically. It demonstrates that you can evaluate AI tools, assess return on investment (ROI), and scale adoption responsibly across the enterprise. It also shows that you can envision new ideas with Copilot and use AI to reimagine processes and unlock growth. Is this the right Certification for you? This Certification is designed for business leaders who are interested in driving transformation and innovation. It emphasizes AI fluency, strategic vision, and leadership in AI projects, but it doesnโt require coding or deep technical expertise. As a candidate for this Certification, you should be able to evaluate AI opportunities, encourage responsible adoption, and ensure alignment of AI strategies with your organizationโs goals. You should be familiar with Microsoft 365, Azure AI services, and general AI concepts. Ready to prove your skills? Take advantage of the discounted beta exam offer. The first 300 people who take Exam AB-731 (beta), on or before December 11, 2025, can get 80% off market price. To receive the discount, when you register for the exam and are prompted for payment, use code AB731Markers25. This is not a private access code. The seats are offered on a first-come, first-served basis. As noted, you must take the exam on or before December 11, 2025. Please note that this beta exam is not available in Turkey, Pakistan, India, or China. Get ready to take Exam AB-731 (beta): Review the Exam AB-731 (beta) exam page for details. The Exam AB-731 study guide explores key topics covered in the exam. Want even more in-depth, instructor-led training? Connect with Microsoft Training Services Partners in your area for in-person offerings. Instructor-led training for this exam will be available starting December 16th, 2025. Need other preparation ideas? Check out Just How Does One Prepare for Beta Exams? Did you know that you can take any Microsoft Certification exam online? Taking your exam from home or the office can be more convenient and less stressful than traveling to a test centerโespecially when you know what to expect. To find out more, read Online proctored exams: What to expect and how to prepare. The rescore process starts on the day an exam goes live, and final scores for beta exams are released approximately 10 days after that. For details on the timing of beta exam rescoring and results, check out Creating high-quality exams: The path from beta to live. Ready to get started? Remember, the number of spots is limited to the first 300 candidates taking Exam AB-731 (beta) on or before December 11, 2025. Stay tuned for general availability of this Certification in February 2026. Learn more about Microsoft Credentials. Related announcements We recently migrated our subject matter expert (SME) database to LinkedIn. To be notified of beta exam availability or opportunities to help with the development of exam, assessment, or learning content, sign up today for the Microsoft Worldwide Learning SME Group for Credentials.
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Every day, it seems like thereโs a new AI tool making headlines. In fact, this year alone, thousands of new AI-powered apps and platforms have launchedโreshaping how we work, create, and solve problems. Instead of tech that demands more attention, weโre focused on AI that helps you make better decisions and gives you the skills to grow your career and your business. Work Change Report: AI is Coming to Work. January 2025. All this innovation makes one thing clear: evolving your skills at the pace businesses expect is essentialโand really challenging. With over 3.6[1] billion people in the global workforce, organizations and individuals everywhere are grappling with the same question: How do we keep pace with AI? Itโs not just a technical challengeโitโs a human one. With the steady stream of new courses, articles, and videos, finding exactly what you needโin the right format, with the right depth, and ready to share with your teamโcan feel overwhelming. Weโve heard from business leaders, developers, and employees alike: you want learning thatโs relevant to your roles and projects, easily accessible, and short enough to fit into your busy day. Thatโs why weโre committed to deliver clear, role-based skilling paths and AI-led, human-enhanced skilling in a unified and accessible wayโso teams can adopt AI faster and lead with confidence. Introducing AI Skills Navigator Today, at Microsoft Ignite, weโre releasing the next-generation AI Skills Navigatorโan agentic learning space, bringing together AI-powered skilling experiences and credentials that help individuals build career skills and organizations worldwide accelerate their business. This is a smarter, more personalized way to build both technology skills and the uniquely human skills required to set yourself apart in an AI-dominated workplace. A single, unified experience: Build and verify your skills with AI and cloud content and credentials from Microsoft, LinkedIn Learning, and GitHubโall in one spot. Personalized recommendations: Get learning content curated just for youโbased on your role, goals, and learning style, whether you prefer videos, guides, or hands-on labs. Innovative learning experiences: Immerse yourself in interactive skilling sessionsโvideos of human instructors, combined with real-time agentic AI coaching. Watch, engage, and understand concepts more deeply, like you would in a live classroom. Learn the way you like: Prefer to listen? Instantly convert skilling materials into AI-generated podcasts to fit learning effortlessly into your day. Custom, shareable skilling playlists: Use AI to build tailored learning paths that you can easily assign to your team or share with your friendsโand track their progressโturning upskilling into a collaborative social experience. & Digital Skills, Vodafone. AI Skills Navigator is now available to everyone around the world as a public preview! For now, all features are in English, but stay tunedโweโre working quickly to add more features and languages, so you can keep growing your skillsโwherever you are. Showcasing expertise in action Learning new skills is important. Proving to employers that you have them is just as critical. Thatโs why weโre expanding Microsoft Credentialsโtrusted for over 30 yearsโto help you verify your real-world skills in AI, cloud, and security. Whether youโre looking to stand out in your career or find the right employee to build out your team, our credentials are here to help highlight and verify great talent. Gartner Unveils Top Predictions for IT Organizations and Users in 2026 and Beyond (press release). October 21, 2025. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. Hereโs how weโre evolving Microsoft Credentials: New AI credentials for business professionals, leaders, and early-career talent. More technical credentials focused on secure, scalable AI solutions. Flexible, short-form training content and skills validation for busy schedules. Unlocking human potential with strategic partnerships We know that building AI skills is a team effort. Thatโs why weโre partnering with leaders like LinkedIn, GitHub, and Pearson to bring you even more ways to learn and grow. Together, weโre making sure you have the resources and support you needโno matter your industry or role. LinkedIn and Microsoft are working together to set a new global standard for AI upskilling. With AI Skills Navigator, you find curated LinkedIn Learning courses that blend essential human and AI skills for every business and technical role. Whether youโre in marketing, finance, HR, operations, or IT, discover practical training that helps you stay ahead. This is just the beginning. Weโll continue to bring you more learning that helps you build professional and leadership skills. GitHub and Microsoft are making it even easier for developers to grow and shine in the AI era. By joining forces within AI Skills Navigator, weโre opening the door for over 100 million developers worldwide to build, prove, and keep expanding their AI skills. Our ongoing partnership is all about nurturing a vibrant developer community that is ready to innovate and keep pace with the fast-changing world of AI. Pearson and Microsoft are teaming up to make it easier than ever to earn and showcase your skills. Credly by Pearson enables professionals to validate their knowledge and gain recognition for their expertise through globally recognized digital credentials. With over 120 million credentials issued and rapid growth in areas like AI, Azure, and cybersecurity, this partnership will empower people to develop in-demand skills and advance their careers. This capability is coming soon. When itโs launched, all Microsoft Credentials will be published to Credly, giving learners a seamless way to earn, manage, and share their achievements. As these exciting partnerships continue to grow, weโre grateful for our Training Services Partners and their long-standing expertise in professional skillingโtailored, human-led training that helps people and organizations everywhere achieve real, impactful results. Helping build careers and businesses, one skill at a time AI combined with your ambition creates a future of tremendous opportunity for you. The hardest part is knowing where to start and direct your focus as the world moves so quickly around us. This is where we can helpโwhether youโre just getting started or youโre already far along the AI learning path. Together, weโre building a world where everyone can grow, evolve, and lead with confidence. The real frontier isnโt about technologyโitโs about what people like you can achieve with it. And weโre here to help you get there, one skill at a time. [1] The World Bank: Labor force, total. Data source: ILO, OECD, and World Bank estimates.APAC Fabric Engineering Connection Call
๐ Happy New Year to all the amazing Microsoft partners I've had the privilege to work with during 2025. I'm excited to announce the first presenter of 2026 for this week's Fabric Engineering Connection call! Join us Thursday, January 8, from 1โ2 am UTC (APAC) for an insightful session from Benny Austin. This weekโs focus: ๐ฏ Updates and Enhancements made to the Fabric Accelerator This is your opportunity to learn more, ask questions, and provide feedback. To participate in the call, you must be a member of the Fabric Partner Community Teams channel. To join, complete the participation form at https://aka.ms/JoinFabricPartnerCommunity. We look forward to seeing you at the call!Americas & EMEA Fabric Engineering Connection
๐ Happy New Year to all the amazing Microsoft partners I've had the privilege to work with during 2025. I'm excited to announce the first presenters of 2026 for this week's Fabric Engineering Connection calls! Join us Wednesday, January 7, from 8โ9 am PT (Americas & EMEA) for an insightful session from Yaron Canari: Discover, Manage and Govern Fabric Data with OneLake Catalog. This is your opportunity to learn more, ask questions, and provide feedback. To participate in the call, you must be a member of the Fabric Partner Community Teams channel. To join, complete the participation form at https://aka.ms/JoinFabricPartnerCommunity. We look forward to seeing you at the calls!
๐๐ ๐๐ฌ ๐๐จ๐ญ ๐ญ๐ก๐ ๐๐ข๐ฌ๐ค. ๐๐ง๐ ๐จ๐ฏ๐๐ซ๐ง๐๐ ๐๐ ๐๐ฌ
This blog explores why the real danger lies not in adopting AI, but in deploying it without clear governance, ownership, and operational readiness. Learn how modern AI governance enables speed, trust, and resilienceโtransforming AI from a risk multiplier into a reliable business accelerator.
