Syncing Multiple Azure DevOps Orgs to One ServiceNow Instance Without Forcing a Shared Workflow
If your organization runs more than one Azure DevOps org, whether from an acquisition, a spun-up subsidiary, or business units that never consolidated onto one instance, you already know the visibility gap. Central ServiceNow has no idea what's happening in any of them unless someone checks manually. Your team ends up pulling status updates by hand, chasing changes across orgs, and reconciling what got closed where. That works well for a couple of orgs, but it falls apart past that. Why a Shared Workflow Usually Creates a Bigger Problem Migrating everyone onto a single Azure DevOps org would close the visibility gap on paper. Each org's area paths, iterations, states, and processes took years to get right, and a forced migration undoes all of it. A sync layer between each Azure DevOps org and your central ServiceNow instance closes the same gap without touching how any individual org works day to day. Each org keeps its own configuration. ServiceNow ends up with a rolled-up view across all of them. Common Use Cases Post-Acquisition Org Sprawl Current Setup: A company acquires another company, or runs several business units, each with its own Azure DevOps org and its own way of working. Problem: Central ops has no single view across orgs, and checking each one by hand doesn't scale past a few teams. Solution: Connect each Azure DevOps org to the central ServiceNow instance separately, each with its own sync rules. ServiceNow gets one rolled-up view, and no org has to change how it works. Bi-Directional Status Sync Between Delivery and Support Current Setup: Support logs incidents in ServiceNow. Development tracks the corresponding work in Azure DevOps, sometimes across several orgs. Problem: Support has to ask developers for status or check Azure DevOps boards directly, and developers end up relaying the same update twice. Solution: Sync status, comments, and priority both ways, so an update in either system shows up automatically on the other side. Field-Level Control Per Org Current Setup: Each business unit or subsidiary has its own rules about what data can leave its Azure DevOps org. Problem: A single shared integration with one set of mapping rules risks exposing fields an org never agreed to share outside its own boundary. Solution: Give each org's connection its own outgoing rules, so a subsidiary decides exactly which fields leave its Azure DevOps org, field by field. Handling Closed and Read-Only Work Items Current Setup: ServiceNow blocks writes to closed incidents through ACLs, and Azure DevOps can hit a similar restriction on closed or read-only work items. Problem: A sync that keeps trying to write to a closed item throws the same error repeatedly, and the real problems get buried under the noise. Solution: Filter closed and read-only states out of the sync, or let the errors surface if operations wants visibility into them. What to Evaluate When Choosing an Approach A few criteria matter more than others once you're running this across multiple orgs. Decentralized configuration: does each Azure DevOps org get its own connection and its own rules, or does everything route through one shared setup? Filtering: can you scope the sync with something like WIQL queries on the Azure DevOps side, by area path, iteration, work item type, or tag? Field mapping: does it handle the difference between ServiceNow's field structure and Azure DevOps work item fields without dropping data? Common pairs are ServiceNow State to Azure DevOps State, ServiceNow Priority to Azure DevOps Priority, and ServiceNow Assignment Group to Azure DevOps Area Path. Custom fields usually need explicit mapping rules. Conflict handling: what happens when both sides update the same field at the same time, and what happens with closed or read-only items specifically? Security: Entra ID or OAuth authentication, PAT management per org, role-based access, audit logging, and whatever compliance certifications your security team asks for during review. Direction: bidirectional where both teams update shared fields, one-way where only one side should ever write. Technical Approaches Service Hooks and REST APIs Azure DevOps Service Hooks paired with the ServiceNow REST API give you sync in both directions. A change in Azure DevOps triggers a Service Hook, which calls the ServiceNow API to update the record, and the same flow runs in reverse. This is the most direct route if you're comfortable building and maintaining the webhook logic yourself. Custom Middleware For anything more complex, custom middleware gives you full control over field transformation, routing, and error handling. Azure Functions, Logic Apps, or a small Node.js or Python service usually does the job. The trade-off is maintenance. You own the retry logic, the error handling, and every update when either platform changes its API. Dedicated Integration Platforms Plenty of teams skip building this from scratch and use a dedicated integration platform instead. These typically come with pre-built connectors for both Azure DevOps and ServiceNow, a way to configure field mapping and filters without writing much code, and managed infrastructure so you're not hosting your own sync server. What they usually cover: Pre-configured connectors that already understand both platforms' data structures Visual or scripting configuration for field mapping and filters Managed infrastructure, so nothing runs on your own servers Built-in retry and error handling for API failures Audit logging for tracking what synced and when Support for multi-org routing and conditional logic out of the box The trade-off runs the other way: a subscription cost instead of a one-time build, less control over the exact implementation, and your data passing through a third party's infrastructure. For teams running more than 2 or 3 orgs against one ServiceNow instance, this usually ends up being less overhead than maintaining custom middleware long-term. Every org here has probably solved a version of this differently. Curious what's worked for you, especially with 3 or more Azure DevOps orgs feeding into one ServiceNow instance, and which part of the setup gave you the most trouble.
Passkey best practices for Windows 365 users on unmanaged devices (BYOD)?
I have global contractors who work from home so spinning up Windows 365 Enterprise Cloud PCs are ideal. The contractors work from from either their personal Mac or personal Windows PC using the Microsoft Windows app so each user can access their dedicated Windows 365 Cloud PC and everything has been working well so far using standard MFA. We are looking to move everyone to passkeys but there is no intuitive documentation on how end users are expected to adopt it so they can use passkeys to 1) access the virtual PC and then use passkeys while in the virtual PC itself. Seems Microsoft explains everything under the hood except how to practically roll it out to reliably replace standard MFA. What is the recommended path forward to migrate my BYOD virtual PC users to passkeys? Thanks, Charlie
Windows 365 and developer environments: how do you balance security and productivity?
Hi everyone, I’d like to raise a topic that we are currently struggling with, and I suspect many other organizations are facing the same challenge. We are in the process of establishing a Windows 365–based development environment, where developers work in Cloud PCs. This is largely driven by: a BYOD strategy security requirements (no sensitive code on unmanaged devices) the need for standardization However, this quickly becomes complex in practice. The core challenge We are trying to balance three competing priorities: 1. Security requirements No sensitive code on local devices Minimal attack surface Zero Trust principles and Conditional Access Full traceability of identity and actions 2. Developer needs Local admin rights to be able to do their work Freedom to install tools, SDKs, and runtimes Flexibility without constant blocking Fast iteration cycles The reality is that if it takes too long to get access or permissions, it breaks the developer workflow. 3. IT and governance Standardization of environments Manageability and patching License and cost control Compliance and auditability The practical dilemma Developers want to be local admins on their machines Security teams prefer: Just-In-Time access (PIM), or No admin privileges at all In practice: PIM tends not to work well for developers It introduces too much friction It disrupts flow and often leads to workarounds What we are currently exploring We are testing a model where: Developers work in Windows 365 Cloud PCs They use their regular corporate identity (Entra ID) Isolation is achieved through the environment, not separate accounts Developers have local admin rights within the Cloud PC However, this raises a new question: How do we secure an environment where the user is an admin? Questions to the community I would really appreciate insights from others who have been through similar scenarios: 1. Identity vs privilege Do you use the same identity for everything, or separate user/admin accounts? How far do you take identity separation? 2. Local admin rights Do you allow developers to have local admin rights? Is it permanent or Just-In-Time? If JIT, how do you make it work without impacting productivity? 3. Cloud-based development environments If you are using Windows 365, Dev Box, or AVD: Has this made it easier to relax restrictions? Or are you facing the same challenges, just in the cloud? 4. Guardrails instead of restrictions Instead of trying to prevent everything: EDR / endpoint protection Conditional Access Network isolation Monitoring and detection Has anyone successfully shifted from strict control to strong guardrails and detection? Current reflection I am starting to think that: Focusing on secure, isolated environments for development may be more effective than trying to tightly control every individual action. In other words: secure the platform not every single user behavior But this is far from straightforward. Purpose of this discussion The goal is to find a realistic blueprint that: maintains high developer productivity meets security requirements minimizes friction in day-to-day work Not something theoretically perfect, but something that actually works. If you have experience in this area, I would really value your input: what has worked well what has not worked key design decisions you would recommend Thanks in advance.
Why Your Copilot Studio Agent Fails in Production (And How to Fix It)
Most Copilot Studio tutorials show you how to build a chatbot. This post is about something harder: building agents that actually work in production. I architect enterprise agents at a hospitality company — handling customer email triage, HR workflows, helpdesk automation, and reporting pipelines across multiple systems. One of those agents reduced human handling time per customer email from ~12 minutes to under 2 minutes (88% reduction) by orchestrating sentiment analysis, CRM lookups, SOP research via child agents, and response drafting — all before a human agent ever opens the email. Here is what I've learned building at that scale. The Four Layers Every Enterprise Agent Needs Most teams design only the top layer and treat everything else as "we'll figure it out later." By the time the other layers become urgent — usually after an incident — they're too expensive to retrofit. Layer Component Conversation Topics · Entities · Adaptive Cards · NLU Orchestration Agent routing · Context passing · State Integration Connectors · Power Automate · Azure Functions Governance DLP · Auth · ALM · Monitoring · Logging Build the governance layer first. Design the conversation layer last. The demo will be slightly less impressive. The production deployment will be significantly more stable. The Three Mistakes I See Most Often 1. Slot-filling designed for the happy path The default Copilot Studio pattern collects parameters one by one. It breaks the moment your flow has conditional branches — which every real enterprise workflow does. Use intent-first routing instead: identify what the user wants before collecting any parameters, then branch to a sub-flow that collects only what that variant needs. 2. Multi-agent context that gets dropped When you delegate from a router agent to a capability agent, the receiving agent needs to know who the user is and what conversation state to preserve. Native session variables don't cross agent boundaries. Build an explicit context envelope — a JSON object passed at delegation time — that carries user identity, security scope, origin topic, and return context. Your agents become stateless with respect to each other. Context travels with the conversation. 3. No async pattern for slow integrations A synchronous request that works for a REST API returning in 200ms will silently fail for a legacy system query that takes 45 seconds. Design async from day one: submit to an Azure Service Bus queue, return a correlation ID, acknowledge the user, and use proactive messaging to deliver the result when it's ready. This is the single biggest gap between demos and production deployments. A Note on Authentication — Chatbots vs. Autonomous Agents This is a distinction most articles get wrong, so it's worth being explicit. Chatbots have a human on the other end of the conversation. Authentication options here include Entra ID SSO (works in Teams and SharePoint channels where the user's identity is delegated to the agent) or client ID + secret (validates against AD but without user delegation — the agent authenticates as itself, not as the user). Autonomous agents are different in a fundamental way: there is no human in the authentication loop. The agent authenticates using the identity of the account that owns and runs it. There is no SSO because there is no interactive user session. This distinction matters because the security model shifts entirely — you are no longer protecting a user session, you are protecting a service identity. This gets more interesting when your autonomous agent connects to non-Microsoft systems. There is no universal pattern here — it depends entirely on what the external system supports: - API Key / Secret — the most common pattern for SaaS integrations. The external system issues a scoped key specifically for this integration. Store it in Azure Key Vault or encrypted Power Platform environment variables, never hardcoded in a flow. The scoping question is critical: is this a full-admin key or a least-privilege key issued only for what this agent needs? - OAuth 2.0 Client Credentials (machine-to-machine) — the agent authenticates as itself using client ID + secret against the external system's auth server and receives a bearer token. No user involved, fully automated. - Basic Auth on legacy systems — still common in enterprise environments. Credentials must live in Key Vault, not in flow variables or connector configuration in plain text. - Custom connector with encrypted connection — Power Platform manages the auth at the connector level; credentials are stored encrypted and scoped to the environment. The governing principle across all of these: the identity the agent uses to call an external system should be issued specifically for that integration, scoped to only the permissions that agent needs, stored securely (Key Vault or encrypted environment variables), and auditable — meaning the external system's logs show the agent's calls as a distinct identity, not a shared admin account that 12 other things also use. Before You Go to Production — Quick Checklist [ ] Autonomous agent's owning account/service principal is scoped to least-privilege — access only to systems the agent needs, nothing broader [ ] Non-Microsoft system credentials stored in Azure Key Vault or encrypted environment variables — never hardcoded in flows [ ] Each external system integration uses a dedicated, scoped credential — not a shared admin account [ ] External system audit logs show the agent as a distinct, identifiable caller [ ] DLP policies configured per environment — production is strict, dev is permissive [ ] Dataverse schema finalized before topic design begins [ ] Error handling designed for every integration point with user-readable failure messages [ ] Async pattern in place for any integration that may take > 10 seconds [ ] ALM pipeline configured: Dev → Test → UAT → Prod with automated solution checker [ ] Application Insights connected with custom events for key agent actions [ ] Escalation rate baseline established with alert threshold configured The One Question to Ask Before Building Anything "What does success look like in six months, and what data does the agent need access to in order to achieve it?" That answer determines your Dataverse schema, your integration architecture, your authentication model, and your DLP policy — before a single topic is created. Agents designed from that question forward are maintainable and trusted by the business. Agents designed from the conversation layer down spend their first year in retrofitting mode. Happy to go deeper on any of these layers in the comments — particularly multi-agent context passing and the async pattern, which I find generate the most questions in enterprise deployments.
Windows Hello for Business - W365 Cloud PC
Hello All, We are currently attempting to use Yubikey USB-A and C devices for authentication. For Laptop authentication it works, but we are trying to implement this on a Windows 365 Cloud PC while signing into Windows App. Has anyone tried this method? This is the device we are trying to use: https://www.yubico.com/product/yubikey-5-series/yubikey-5c-nfc/ This is what i seen when i did my research: High‑level architecture (important context) On Windows 365 Cloud PCs: ✅ WHfB is PIN‑based (this is the supported model) ⚠️ Biometrics (face/fingerprint) are usually not available ✅ WHfB is user‑scoped, not just device‑scoped ✅ WHfB requires a healthy PRT first ❌ WHfB cannot provision if AzureAdPrt = NO If PRT/WAM aren’t healthy, WHfB setup will never appear, even if policy is correct. Any input, insight, or feedback is grreatly appreciated!!Expanding the Reach of AI-enabled Cloud PCs for Frontier Firms
Co-Authored by Lakshmi Rayasam Over the past several months, we’ve been focused on a clear goal: making AI-enabled Cloud PCs easier to adopt, easier to deploy, and available to more customers—wherever they are. Following the momentum from Ignite, releasing Improved Search & Click to Do, we’ve continued to evolve the AI-enabled Cloud PC experience in Frontier Preview, prioritizing scale, accessibility, and product market fit. The latest set of enhancements removes deployment blockers and expands where—and how—customers can get started. We’ve also added a new refreshing AI-enabled end-user experience explaining the value of Frontier while users hover over the AI-enabled (Frontier) tag. Note: Future availability of these features is dependent on the results of this Frontier Preview and is subject to change Removing the need for Windows Insiders AI‑enabled Cloud PCs now work with standard Windows 11 retail builds, eliminating the need for Windows Insider participation. This change alone unlocks broader enterprise deployment scenarios and removes a major barrier for production use. AI-enabled Cloud PCs are just one toggle away as IT admins can join our Frontier Program and enable AI-enabled Cloud PCs via a newly introduced policy setting within the Devices – Onboarding: Windows 365 > User Settings blade, and further filter access based on Microsoft Entra ID group access. Note: After enrolling the latest Windows 11 25H2 (OS Build 26200.7840 or higher) you must reboot your Cloud PC. Expanded regional availability To meet customers where they operate, we’ve expanded AI‑enabled Cloud PCs to additional Azure regions:‑ Japan East Germany West Central South Central US Canada Central This expansion improves latency, supports data residency needs, and enables more global customers to participate in the Frontier Preview. 128GB disk support for Windows 365 Enterprise We’ve added support for 128GB disk size Windows 365 Enterprise licenses, offering greater flexibility for customers who don’t require larger footprints while still enabling AI‑driven workflows. These right‑sized deployments and makes it easier to scale across broader user populations. Together, these updates directly address the most common Ignite feedback and are expected to unlock large‑scale deployments, including enterprise pilots transitioning into sustained usage. What Comes Next Following the March rollout, our CY26 focus shifts to closing parity gaps using cloud‑based models, in close partnership with Windows platform teams. Initial work centers on enabling Text Actions for Click‑to‑Do via a cloud models within Frontier Preview—one of the most requested capabilities from customers.‑‑‑ In parallel, we’ll continue investing in foundational improvements that increase perceived feature depth and usage signals, including cost optimization, reliability enhancements, and productivity scenarios that compound value across the Cloud PC experience.
MS designer is NOT WORKING!
It only makes 1 image not 4, it takes way too long time,. and it doesnt follow prompting,. and it makes imegs in 3:2 not 16:9... thsi has been goign on for over 2 weeks,... alsmot 3.. i have been in contact with support many times woith zero help.. HOW can you have a product in your office bunlde ant it not workign an zero supprot on it? I need teh application to make my videos,. im just abptu to cancel all subscritopons to micrposft an switch to apple..
