'External guests' able to join community?
Hi, We have merged two companies, and until we have one tenant, we are having to add one organisation as guests to one tenant instance of Viva Engage. This means creating a new 'All Company' community for them - everyone will be added. We are also looking at more optional communities (ie an AI community) - is there a way that the 'external guests' can choose to join this community? What would be the best way instead of adding everyone into it?
Viva Engage Topics
Hi all, The org I work for is looking into creating a Values in Action employee recognition program in Viva Engage. From searching I have not been able to find clear answers on our requirements. The requirements are: One dedicated Viva Engage community Recognition-only posts (no general discussion) 5 organizational values Users must select one value when recognizing a colleague We would prefer to use Praise posts Reporting Can Viva Engage be configured to: Restrict users to only 5 approved Topics? Prevent users from creating additional Topics? Require a Topic/Value to be selected before posting? Require or default to Praise posts? If not natively supported, what governance or configuration approach is recommended?
Is Teams especially the class notebook being very slow?
Has anyone else noticed Teams being slower this last two weeks? What about class notebooks? Ours are taking more than an hour to distribute a simple page. Is that just us, or is anyone else experiencing that? What can you suggest to make it faster, how it was previously? Thank you.MS 365 Family Subscription Issues
Using MS 365 Family is very frustrating and a pain. Below is my conversation with Co-pilot. I had to use co-pilot because reaching a real person at Microsoft has not been possible. MS 365 Business version I have works nicely on for years was great. The personal Family version is to scattered, unorganized and seems very disconnected. From the response I got from Co-pilot, it is stated "many" feel the same You said: If "Microsoft has several portals, and many people expect one central dashboard but end up in account or subscription pages instead". Why does Microsoft not simply develop a centralized dashboard to meet customer expectations? Copilot said: That's a fair question, and frankly, many Microsoft 365 Family users share your frustration. My opinion is that Microsoft's challenge is partly historical. What people think of as "Microsoft 365" is actually a collection of services that evolved separately: Microsoft Account (identity, billing, subscriptions) Outlook (email and calendar) OneDrive (storage) Teams (communication) Word, Excel, and PowerPoint (productivity apps) Family Safety (family management) Over many years, these products were developed by different teams and originally had different websites and user experiences. Microsoft has tried to bring them together under "Microsoft 365," but some of the older boundaries remain. From a customer perspective, the expectation is usually much simpler: "I pay for Microsoft 365 Family. Show me one page with all my apps, subscription details, storage, family members, and settings." That expectation is reasonable. Microsoft has moved somewhat in that direction with Microsoft 365 Home, but the experience is not always as unified as consumers expect. Depending on the account type, browser, region, and whether you're using consumer or business services, you can encounter multiple portals that feel disconnected. For many home users, the ideal dashboard would have: All installed applications Recent documents OneDrive storage status Family members Subscription status Teams and Outlook shortcuts Device management ...all on one screen. Ironically, if you're using a Mac, you may find that the most practical "dashboard" is not on the web at all. It is often: Open Word. Verify your Microsoft account. Use the Microsoft app launcher within the Office apps or your Mac's Applications folder. Based on your comments, it sounds like you're looking for the sort of centralized home screen that Apple provides with an Apple ID account and that Microsoft has not yet fully achieved for Microsoft 365 Family customers.
Viva Engage Notifications
We would like to understand whether Viva Engage supports any of the following capabilities: Automatically enabling email notifications for new members when they join a community. Configuring community members to receive notifications by default and requiring them to opt out rather than opt in. Applying a tenant-wide or community-level policy that enables email notifications for all members of selected communities. Configuring notification settings through Microsoft 365 admin settings, Viva Engage admin settings, PowerShell, Microsoft Graph, or any other administrative mechanism. Identifying whether this functionality is on the Microsoft roadmap if it is not currently available. Our company is currently test piloting Viva Engage with a few different departments, but they are noticing that member engagement is not very high. We are looking for ways to get members more engaged in the product. Thanks, GregHow Azure uses AI to turn feedback into improved customer experience
Authors: eshaanbhattad, jenniferjhan, lakshminarasimha, bharadwajr The Challenge: Synthesizing fragmented feedback signals, to improve Azure's experience quality at scale Customers experience products and services end to end, but product experiences are often structured around individual service. One team may only know its top issues, while another may see only its own slice of experience. That structure makes it difficult to identify cross-cutting friction across the broader product experience. The feedback signals themselves are also fragmented. Customers share feedback through in-product surveys, support cases, field conversations, and social channels. Most product teams can see only part of that picture, making it hard to distinguish isolated comments from meaningful trends, understand which issues were having the greatest impact, and avoid missing critical feedback. For the Azure team, the challenges of fragmentation were amplified by scale. The team processed roughly 10,000 to 15,000 customer feedback reports each month, and synthesizing that feedback required 80 to 100 hours of expert analysis. Additional effort was needed to translate findings into consistent engineering work items. As feedback volume grew, manual analysis became increasingly unsustainable creating delays in identifying and addressing customer priorities. Compounding the challenge was the absence of an effective feedback loop to measure the impact of quality improvements. Teams struggled to justify investments in quality over new features because the return on those investments was difficult to quantify. The absence of a closed-loop measurement system made it difficult to consistently assess the customer impact of quality improvements. The team needed a system that could operate across organizational boundaries and across the product development lifecycle: Identify the most critical customer issues across fragmented feedback channels and product areas. Convert those insights into actionable engineering work, help teams address issues effectively, and measure outcomes to close the loop. The solution needed to preserve team-specific context, maintain auditability, continuously improve through feedback, and keep human experts in control of decisions that require judgment. To address these challenges, Microsoft launched the Great Experiences Matter (GEM) initiative. GEM is designed to analyze feedback signals in aggregate, with access controls and privacy safeguards designed to limit exposure of customer-identifiable information while helping teams identify patterns across channels. The Solution: an agentic feedback-to-fix loop with humans in control GEM created an AI-enabled feedback-to-fix workflow that connects customer listening, engineering action, and impact measurement across the product development lifecycle. The workflow uses Microsoft Foundry, Azure Data Explorer, Microsoft Fabric, Azure DevOps, and a set of custom agents to transform large volumes of qualitative feedback into prioritized insights and actionable engineering work. Fig 1. GEM AI-enabled automation workflow The system closes the loop through two connected motions. Find. Agentic workflows remove noise and duplicate reports, assess relevance and actionability, classify feedback against known issues from UX research, cluster related issues, and surface likely root causes. GEM builds on years of deep end-to-end UX research that has identified systemic friction across customer journeys and product boundaries. By continuously triangulating GEM signals with ongoing research, we combine broad, scalable listening with deep human insight to inform a more cohesive Azure experience. The results are surfaced through global scorecards for cross-cutting Azure issues and vertical scorecards tailored to individual product teams. Fig 2. GEM Global scorecard of top issues with Azure, data has been fictionalized to protect intellectual property Fix. The workflow creates Azure DevOps work items with the customer context, likely reproduction steps, recommended next actions, and an auditable trace of the supporting analysis. To date, 42% of the identified issues have been addressed through engineering action. The team is also extending an AI-assisted engineering workflow, using GitHub Copilot cloud agent, that can generate proposed fixes for straightforward issues. Engineers remain responsible for reviewing, refining, approving, and shipping any changes. The architecture is designed for inspection rather than blind automation. The recommendations include an auditable evidence trail allowing reviewers to inspect the source feedback, classifications, supporting references, confidence indicators, and recommendation actions. Human expertise enters the system at several points. Researchers shape the issue taxonomies and qualitative grounding. Product teams define ownership boundaries, business priorities, domain-specific vocabulary, and trusted sources that guide agent analysis. Engineers review and act on resulting work items, while leaders use scorecards to inform investment decisions. This context is captured in configuration files that evolve alongside the products they support. Teams can add new issue categories, refine keywords, clarify ownership boundaries, or identify trusted research sources. The next analysis cycle automatically incorporates the updated context without requiring changes to the underlying agents. That design creates a "feedback loop for the feedback loop". Teams review the root-cause analyses and work-item quality, identify gaps, and refine their configurations. This enables teams to continuously embed domain expertise into the workflow, improving how feedback is interpreted and prioritized without requiring changes to the underlying infrastructure. Their input improves subsequent runs, helping the system become more precise while preserving local product knowledge. The agent also maintains access to reports from previous runs and uses tools such as Web IQ and MCP servers to assess whether previously identified issues are improving, still require attention, or can be confidently closed. Fig 3. Example of the vertical feedback agent reasoning through customer feedback to find new work items One demonstrated example surfaced customer reports that a networking tool lacked IPv6 validation and support. The workflow generated an engineering work item describing the issue, customer impact, likely reproduction path, and recommended actions. The networking team reproduced the issue, validated the finding, and added it to its backlog. The goal is not to remove people from the process. Agents assume much of the cognitive load associated with sorting, clustering, tracing, and drafting, allowing experts to focus on judgment, prioritization, and implementation. Teams remain accountable for what is fixed, what is funded, and what is allowed to ship. The Impact: measurable experience gains at Microsoft production scale GEM began with manual interventions and is now scaling through AI-enabled workflows. The combined approach has produced measurable results across the Azure Portal and individual product experiences: The workflow aggregates and analyzes approximately 10,000 to 15,000 feedback reports each month across in-product, support, and social channels. Automated analysis reduced manual synthesis time by over 95 percent, turning a process that required 110 to 160 hours each month into a workflow that runs in under 60 minutes. Between October 2025 and April 2026, Azure Portal feedback rates declined by 30%. During that same period, GEM helped teams identify and prioritize experience improvements, creating a clearer link between customer feedback, engineering action, and outcome measurement. Service-level outcomes also demonstrate how better signals can drive business impact. For example, improvements to VM Connect experiences reduced overall Core Compute support volume by 1-2% per month, resulting in proportionate cost savings. The Azure Growth team increased subscription conversion by 9.1 percent after prioritizing issues highlighted through GEM. The value goes beyond speed. Leaders gain a more consistent basis for prioritization, and engineering teams receive work that is already connected to customer evidence and impact signals. Most importantly, every completed cycle creates new learning. Teams can measure changes in customer feedback and support volumes following improvements, incorporate partner input into future analyses, and continuously refine both the system and the products it helps improve. Key learnings and transferable practices The GEM experience offers several lessons for teams building agentic systems around complex, qualitative business processes: Start with real problems, not AI - Value comes from understanding the genuine business needs and applying AI where it is demonstrably better than existing approaches. Applying AI without a clearly defined problem often adds complexity without delivering meaningful value. Design for the end-to-end workflow - Value comes from connecting insights to the broader business process, including grounding in prior knowledge, prioritization, engineering action, post-fix measurement and reporting. Standalone AI output creates limited value, while an integrated workflow drives outcomes. Design for human judgment and accountability - Agents can reduce toil and cognitive load, but researchers, product managers, engineers, and leaders remain responsible for validating insights and determining appropriate actions. Ground agents in the knowledge of the teams they serve - Shared models require local context. Editable configuration files allow teams to define ownership, business priorities, releases, examples, and trusted sources without modifying underlying agents. Build observability and feedback mechanisms into the agentic system itself - Making analysis inspectable through reasoning traces, source context, and recommendations enables experts to identify gaps, improve outputs, and build trust over time. Build feedback mechanisms directly into the flow of work, making it effortless for users to provide input on the system. Tailor outputs to the people making decisions - Executives need trends and investment signals. Researchers need evidence and themes. Engineers need reproducible, actionable work. Effective systems deliver the right information to the right audience. Start small and iterate quickly - The AI landscape continues to evolve rapidly. Begin with a well-defined problem, measure outcomes, learn from feedback, and iterate as capabilities mature. Looking forward GEM continues to scale across the Azure Portal ecosystem. In addition to the global scorecard, vertical scorecards are now live with seven teams, expanding to the top 20 portal extensions representing more than 80% of portal traffic and feedback, with longer-term plans to extend coverage across the entire ecosystem. The roadmap includes expanded feedback ingestion, streamlined work-item tracking, AI-assisted remediation workflows, stronger evaluation, and a self-improving architecture. Proposed fixes would remain subject to engineer review, approval, and standard release controls before deployment. GEM is also developing AI-assisted pre-release governance workflows for production code that help identify potential quality issues during development. We will share more about these pre-release workflows in a future post. New tools and models will continue to evolve, but the enduring principle remains the same: combine enterprise-scale automation with clear ownership, trusted grounding, and human control. For Microsoft, Customer Zero means deploying these systems in real production environments, learning from the complexities, and sharing those lessons broadly. GEM shows what becomes possible when AI does more than summarize feedback. It helps an organization listen, act, measure outcomes, and continuously learn at customer scale. Microsoft's Customer Zero blog series gives an insider view of how Microsoft builds and operates Microsoft using our trusted, enterprise-grade agentic platform. Learn best practices from our engineering teams through real-world lessons, architectural patterns, and operational strategies for building, operating, and scaling AI-powered systems across the organization.SharePoint Showcase: 10 Custom AI Skills Every SharePoint Site Owner Should Build
In this edition of SharePoint Showcase, we explore how skills work, how to create or install them, and ten practical examples to help SharePoint site owners get started. These examples are not an exhaustive list, but a curated starting point for identifying everyday processes that can become reusable, team-ready skills.Wiring Azure DevOps Pipeline Templates Without the Parameter Sprawl: The Manifest Facade Pattern
Where reusable pipelines start to hurt Who this is for: Platform and DevOps engineers who build shared Azure DevOps Pipeline Templates and want other teams to adopt them consistently. If you have ever built a set of shared Azure DevOps Pipeline Templates, you probably know how this goes. You write a whole library of clean, well-factored templates: provisioning infrastructure, deploying apps, scanning, promotion, and more. Each one is tidy on its own. Then the first team tries to actually use them together, and things get messy fast. That is exactly what happened to us on a customer engagement, while building a DevOps framework for their engineering teams. The templates themselves were fine. The trouble was wiring them together. Every consumer pipeline had to know how to call each template it used: the right order, which values one template fed another, and the full parameter list for every one of them. As the library grew, those consumer pipelines grew with it, long and repetitive. That friction turned into a real adoption problem. Getting started meant wading through pages of parameters, so teams put it off, copied whatever the team next door had, or quietly built their own thing instead. This post follows that story, from the tangle of parameters to the solution we landed on. Here is how it comes together. Where we started: Good templates, hard to adopt The DevOps framework we built had many templates. To keep this walkthrough concrete, we will follow just two of them: one that provisions infrastructure with Terragrunt (a wrapper around Terraform), and one that deploys an app to Kubernetes with Helm. Both are trimmed down to the essentials here so they are easy to follow. The infrastructure template, templates/infra/terragrunt-deploy.yml : parameters: - name: stack type: string - name: workingDir type: string steps: - script: | cd ${{ parameters.workingDir }} terragrunt plan -out=tfplan terragrunt apply -auto-approve tfplan displayName: "Deploy ${{ parameters.stack }}" The application template, templates/app/helm-deploy.yml : parameters: - name: releaseName type: string - name: chart type: string - name: namespace type: string - name: imageTag type: string steps: - script: | helm upgrade --install ${{ parameters.releaseName }} ${{ parameters.chart }} \ --namespace ${{ parameters.namespace }} \ --set image.tag=${{ parameters.imageTag }} displayName: "Deploy ${{ parameters.releaseName }}" There is nothing wrong with either file. The pain showed up in the pipeline that had to use them, where a team had to stitch the templates together by hand, remember the order, and repeat that boilerplate for every environment. Do that across a dozen services and you get long, copy-pasted pipelines that teams struggle to adopt. And because the wiring is done by hand, it leaves loopholes: a team can skip a step or override a parameter and slip past the guardrails you thought were in place. Challenge 1: Wiring the templates together The obvious move was to hide the wiring behind a single template that owned the order and the plumbing. We called it the orchestrator template: a consumer pipeline called that one template, and it wired up the rest. # templates/deployment-orchestrator.yml (the single orchestrator) parameters: - name: infraStack type: string - name: infraWorkingDir type: string - name: releaseName type: string - name: chart type: string - name: namespace type: string - name: imageTag type: string # ...and this list just kept growing stages: - stage: Infra jobs: - job: infra steps: - template: infra/terragrunt-deploy.yml parameters: stack: ${{ parameters.infraStack }} workingDir: ${{ parameters.infraWorkingDir }} - stage: App dependsOn: Infra jobs: - job: app steps: - template: app/helm-deploy.yml parameters: releaseName: ${{ parameters.releaseName }} chart: ${{ parameters.chart }} namespace: ${{ parameters.namespace }} imageTag: ${{ parameters.imageTag }} A team used it by calling that one template and passing a value for every parameter it exposed: # azure-pipelines.yml (a team's pipeline) parameters: - name: imageTag type: string extends: template: templates/deployment-orchestrator.yml parameters: infraStack: network infraWorkingDir: infra/network releaseName: orders-api chart: charts/orders-api namespace: orders imageTag: ${{ parameters.imageTag }} # ...and a value for every other parameter, too This solved the ordering and the copy-paste, but it handed us a new headache. This one template now had to expose every parameter of every template underneath it, and the list grew each time we added a capability. Worse, a real service usually needed more than one infrastructure stack and more than one Helm release. A flat list of parameters cannot express “two infrastructure stacks and three Helm releases” without silly names like infraStack1 , infraStack2 , and so on. Nobody could learn the thing. We had solved the wiring, only to trade it for a parameter problem. Challenge 2: The orchestrator’s parameter list explodes So how do you shrink that list? Step back and ask what you are really describing: a set of things to deploy. So the input should describe that set, not a long flat list of loose values. That is where the deployment manifest came in: one small config that lists the infrastructure to create and the apps to deploy. It reads about how you would expect: infrastructure: - stack: network workingDir: infra/network applications: - releaseName: orders-api chart: charts/orders-api namespace: orders Now the orchestrator can take that single manifest and loop over it, instead of exposing dozens of separate parameters. Its parameter list collapses to one, and a ${{ each }} loop turns each entry into a stage or job: # templates/deployment-orchestrator.yml (the orchestrator, now manifest-driven) parameters: - name: manifest type: object - name: imageTag type: string stages: - stage: Infra jobs: - ${{ each stack in parameters.manifest.infrastructure }}: - job: infra_${{ stack.stack }} steps: - template: infra/terragrunt-deploy.yml parameters: stack: ${{ stack.stack }} workingDir: ${{ stack.workingDir }} # ...an App stage loops over parameters.manifest.applications the same way And a consumer passes that manifest straight to the orchestrator: # azure-pipelines.yml (a team's pipeline) parameters: - name: imageTag type: string extends: template: templates/deployment-orchestrator.yml parameters: imageTag: ${{ parameters.imageTag }} manifest: infrastructure: - stack: network workingDir: infra/network applications: - releaseName: orders-api chart: charts/orders-api namespace: orders The parameter problem is solved. But hand-writing that whole manifest inside every pipeline is a lot to repeat, so the natural instinct is to pull it out into its own file. That is where things get tricky. Challenge 3: The manifest is read too late to shape the pipeline So we tried exactly that: we moved the manifest into a deployment-manifest.yml file and had the orchestrator read it back and expand it into stages and jobs. It sounded reasonable, but it did not work. To see why, you need to know how Azure DevOps builds a pipeline before it runs anything. An Azure DevOps pipeline actually happens in two phases, and they are further apart than people expect. First comes the build phase, before anything runs. Azure DevOps reads your YAML, pulls in every template, evaluates every ${{ }} expression, and unrolls every ${{ each }} loop. Out of this it produces one final, fully assembled pipeline. At this point no agent has started and no repo has been checked out. Only parameters and template expressions exist yet. Then comes the run phase. The assembled pipeline runs. An agent starts, checks out your code, and only now can a script open a file on disk. Here is the problem. Your deployment-manifest.yml does not exist as far as the pipeline is concerned until an agent checks out the repo, and that checkout happens during the run phase. So if the manifest is supposed to decide how many stages there are, or how many apps each get their own job, that decision has to be made earlier, while the pipeline is still being built. Important: The shape of the pipeline, its stages, jobs, and loops, is locked in while the pipeline is being built. A file you read during the run comes too late to change any of it. That was the wall we hit. The manifest was the right idea, but reading it from a file happened too late for the orchestrator to turn it into stages and jobs. So the manifest could not come from a file read during the run phase. It had to already exist, as an object, before the pipeline was assembled. The Manifest Facade pattern: Build the config while the pipeline is assembled The solution is to stop thinking of the manifest as a file to read, and start thinking of it as an object you build while the pipeline is being assembled. Parameters are available then. Template expressions can build a whole object then. So we added a small template, kept in the team’s own repo, with one job: take a couple of simple inputs, build the full manifest from them, and pass it to the shared orchestrator. We called it the builder, since assembling the manifest is its only job. It stays thin and lives next to the team’s pipeline, while the orchestrator stays in the shared platform repo. This is not a brand-new invention so much as a few familiar ideas working together, applied at pipeline build time. The manifest is a Parameter Object (Martin Fowler’s refactoring for collapsing a long parameter list into one structured value). The orchestrator is a Facade (the Gang of Four pattern for putting one simple, unified interface over a subsystem, here the underlying leaf templates). And the small template in the team’s repo is the piece that assembles that Parameter Object from a couple of inputs. What makes it an Azure DevOps pattern is the timing: the manifest is built as an object while the pipeline is assembled, so it can drive template expansion instead of sitting in a file that only gets read during the run. That is the twist we did not see written down anywhere, so we gave it a name: the Manifest Facade pattern. In practice it comes together in three small pieces: the consumer pipeline references the builder, the builder assembles the manifest and hands it to the shared orchestrator, and the orchestrator expands that manifest into stages and jobs. Here is how the files extend into one another: Here is each piece in turn. Step 1: The consumer references the builder The consumer pipeline extends the builder that lives in its own repo. It also declares the shared platform repo as a resource, so the orchestrator the builder calls is available while the pipeline is built. # azure-pipelines.yml (the team's pipeline) parameters: - name: imageTag displayName: Image tag type: string resources: repositories: - repository: platform type: git name: platform/pipeline-templates ref: refs/tags/v1.0.0 trigger: - main extends: template: config/deployment.yml@self parameters: environment: dev service: orders imageTag: ${{ parameters.imageTag }} Because the image tag is a runtime parameter, it is declared here in the consumer pipeline, which is what makes it appear in the Run pipeline panel for someone to fill in. Beyond that, the team passes only an environment and a service name, and the builder works out the rest. Step 2: The builder assembles the manifest The builder takes those inputs and assembles the whole manifest from them, then hands it to the orchestrator. Every structural value here is a parameter or a template expression, so all of it is ready while the pipeline is being assembled. # config/deployment.yml (builder, in the team's repo) parameters: - name: environment type: string - name: service type: string - name: imageTag type: string extends: template: deployment-orchestrator.yml@platform parameters: imageTag: ${{ parameters.imageTag }} manifest: schemaVersion: v1 infrastructure: - stack: network workingDir: infra/${{ parameters.environment }}/network applications: - releaseName: ${{ parameters.service }} chart: charts/${{ parameters.service }} namespace: ${{ parameters.service }} The builder assembles the manifest inline from the environment and service, then passes it straight to the orchestrator. The image tag is different: it is a runtime parameter the user supplies at queue time, so it is passed through to the orchestrator template and never becomes part of the manifest. The team gets a tiny interface, and the orchestrator still gets the full structure it needs. Step 3: The orchestrator turns the config into stages and jobs The orchestrator takes a single object and loops over it with ${{ each }} . Each stage and job gets generated while the pipeline is assembled. # deployment-orchestrator.yml (shared platform repo) parameters: - name: manifest type: object - name: imageTag type: string stages: # a ValidateManifest stage runs first (covered in the next section) - stage: Infra jobs: - ${{ each stack in parameters.manifest.infrastructure }}: - job: infra_${{ stack.stack }} steps: - template: infra/terragrunt-deploy.yml parameters: stack: ${{ stack.stack }} workingDir: ${{ stack.workingDir }} - stage: App dependsOn: Infra jobs: - ${{ each app in parameters.manifest.applications }}: - job: app_${{ app.releaseName }} steps: - template: app/helm-deploy.yml parameters: releaseName: ${{ app.releaseName }} chart: ${{ app.chart }} namespace: ${{ app.namespace }} imageTag: ${{ parameters.imageTag }} Because the manifest is a real object by the time the loops run, ${{ each }} unrolls it into actual jobs. List two stacks and you get two jobs. List two apps and you get two deploy jobs. And because the team only describes what to deploy, the sensitive wiring like service connections stays inside the platform templates, out of reach. The loophole is gone because the parameter is gone. Why the order of things matters The whole pattern comes down to who does what, and when. The builder turns a simple intent (“deploy orders to dev”) into a full manifest, while the pipeline is being assembled. The orchestrator turns that manifest into real stages and jobs, still while the pipeline is being assembled. Only the leaf templates do the real deployment work during the run: checkout, terragrunt apply , helm upgrade . Nothing about the shape of the pipeline waits for the run, so nothing about it depends on a file that only shows up after checkout. That is the whole trick. If you do have values you genuinely cannot know until the run, like a secret fetched from a vault or an artifact version an earlier stage writes to a variable, those still belong in runtime variables and variable groups. The manifest is for the structure and config you already know when you queue the build, which is almost always the part that was causing the pain. Validating the manifest against a schema Once the manifest became the one interface every team fills in, it needed a contract. We wrote that contract as a JSON Schema and kept it in the shared platform repo under schema/v1/ . The folder name is the version: backward-compatible additions go straight into v1 , and the day we need a breaking change we add a schema/v2/ alongside it, so existing consumers keep working while the schema evolves. Each manifest carries a schemaVersion so the orchestrator knows which contract to hold it to. On top of that, we validate in three layers, each catching a different class of mistake. Layer 1 is build time, for free. Because the orchestrator’s parameters are typed, with manifest declared as an object , Azure DevOps catches a range of structural problems while it expands the templates, before anything runs. For example, if a manifest left out infrastructure or misspelled it, the orchestrator’s ${{ each stack in parameters.manifest.infrastructure }} loop would have nothing valid to iterate over, and the failure would surface during template expansion rather than halfway through a deployment. Layer 2 is a version check that runs at build time. Both the ${{ if }} and parameters.manifest.schemaVersion are resolved while the pipeline is being assembled, so the orchestrator decides right then whether it understands the manifest’s version. If it does not, the only thing it generates is a single failing stage, and the real deployment stages are never built, so nothing runs against a contract the orchestrator does not know: # deployment-orchestrator.yml (shared platform repo) parameters: - name: manifest type: object stages: # Layer 2: reject schema versions this orchestrator does not understand - ${{ if not(containsValue(split('v1', ','), parameters.manifest.schemaVersion)) }}: - stage: UnsupportedSchemaVersion jobs: - job: fail steps: - script: | echo "##vso[task.logissue type=error]Unsupported manifest schemaVersion '${{ parameters.manifest.schemaVersion }}'" exit 1 # the ValidateManifest, Infra, and App stages below are generated only when the version is supported Layer 3 is a run-time check against the full schema. The first stage the orchestrator generates is ValidateManifest . Because the schema lives in the platform repo, the job checks that repo out first. Then it serializes the manifest object to JSON with the convertToJson expression, writes that JSON out to a file, and runs a JSON Schema checker to compare the file against the schema. If the manifest breaks the contract, the pipeline stops here, before any infrastructure or app stage runs: # Layer 3: validate the real manifest object against the JSON Schema - stage: ValidateManifest jobs: - job: validate steps: # the schema lives in the platform repo, so check it out first - checkout: platform - script: | echo '${{ convertToJson(parameters.manifest) }}' > manifest.json pip install check-jsonschema check-jsonschema --schemafile schema/${{ parameters.manifest.schemaVersion }}/deployment.schema.json manifest.json displayName: "Validate manifest against schema" Layer 1 is automatic, Layer 2 rejects an unknown contract at build time so the deployment stages are never generated, and Layer 3 confirms the actual values match the schema before any real work begins. Together they turn “the manifest looked right” into “the manifest is provably valid.” A few trade-offs to keep in mind No pattern comes without trade-offs. A few things worth weighing: The manifest has to be something template expressions can build. You can compose objects, loop, and branch with ${{ if }} , but there is no running arbitrary code while the pipeline is assembled. Anything fancier may need a prep step or a file generated upstream. Every team carries a small builder template. We think that is a fair trade, since it keeps their intent local and readable, but it is one more file per repo. Keep it thin and let the orchestrator hold the real logic. Treat the schema as living documentation. Because the manifest’s JSON Schema spells out every field and what it means, teams can read it to build their own manifest with confidence, instead of reverse-engineering the orchestrator. Pin the shared repo to a tag, like the v1.0.0 above, so a change to the orchestrator does not silently change everyone’s pipeline on the next run. Debugging takes a small shift in habit. When something looks off, use the pipeline’s preview to see the fully assembled YAML before it runs. It shows you exactly what the loops produced. Tip: Use the Azure DevOps pipeline preview to see the fully assembled YAML without running anything. It is the fastest way to confirm your manifest unrolled into the stages and jobs you expected. Wrapping up It is a journey a lot of platform teams will recognize. Clean templates, messy wiring, one orchestrator that fixes the order but drowns in parameters, a config file that brings back sanity, and then the surprise that reading it at the wrong moment means it can never shape the pipeline. The solution is small and it sticks. Put a thin builder template next to each team, let it assemble the manifest from a couple of simple inputs, and let a shared orchestrator turn that manifest into stages and jobs. Teams get an interface they can easily understand and adopt. The platform team keeps the wiring and the guardrails in one place. And the timing gap that trips up so many “just read the config file” attempts stops being a problem, because you are working with it instead of against it. Key takeaways Shared Pipeline Templates stall on adoption when every team has to wire them together by hand. A single orchestrator template fixes the ordering, but a flat parameter list does not scale to real adoption. A config file read during the pipeline run cannot shape the pipeline, because stages and jobs are decided earlier, while the pipeline is assembled. The Manifest Facade pattern builds the config as an object at assembly time, so one small input drives many templates, consistently and with the guardrails baked in. Give the manifest a versioned JSON Schema and validate it in layers, so a broken contract fails fast instead of halfway through a deployment. How are you handling template sprawl in your own pipelines? We would love to hear what has worked for your teams in the comments.
How are you handling performance review data across Teams, Outlook, and SharePoint?
We just wrapped up our mid-year review cycle and it was a mess. Managers had to go through old Teams chat messages to find feedback they gave or go through our sharepoint documents to see where everyones goals were. You think there is a way I can use copilot inside Microsoft Teams to somehow parse through all of this before reviews? I saw this app performance 365 or something like that in the app store but I dont think its an official Microsoft app. Any ideas?
