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Legacy VDI migrations stall when teams do not know which workloads, policies, and user dependencies the old environment actually supports. Scott Manchester, Nerdio’s Chief Product and Technology Officer, argues for discovery before replacement. For AI agents, the parallel is to assign a distinct identity and scope controls to the identity and access path actually in use—not assume one Entra policy governs every credential or data route.
Why legacy VDI migrations get stuck
In a StorageReview interview published October 1, 2026, Tom Fenton describes a common migration problem: administrators inherit environments shaped by undocumented decisions, while workloads and business needs drift from the design those decisions were meant to serve. Replacing the platform without recovering that context can disrupt users or preserve unnecessary complexity in a new environment.
Manchester estimated that roughly 60 million virtual desktop seats remain on legacy infrastructure, including older Citrix and Omnissa Horizon deployments. That is his estimate as reported by StorageReview; the interview provides no methodology, so it should not be treated as an independently measured industry total.
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Start with discovery and workload rationalization
Before choosing a destination, establish what users depend on and whether each workload still needs a virtual desktop. Inventory the environment’s structure, workload profiles, policies, and cost, then connect those findings to actual department and user requirements. This makes the migration a decision about workloads rather than a one-for-one conversion of old desktops.
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The interview describes Nerdio Compass as a free, agentless discovery tool in public preview at the time of publication. It was reported to read Citrix, Azure Virtual Desktop (AVD), or Intune estates and surface environment structure, workload profiles, policy configuration, and cost. Omnissa Horizon support was described as being on the roadmap, not as shipped. These are interview-time product claims; confirm current availability, supported sources, and pricing with Nerdio before relying on them.
Choose a VDI destination by workload, not by habit
The interview identifies three possible destinations. It does not provide a quantitative comparison or establish that any one option is right for a particular workload. Use the following questions to test fit against discovery findings and observed usage.
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| Destination | Questions to answer | Migration checks |
|---|---|---|
| AVD multi-session pools | Can users with similar needs share pooled capacity, or do their applications and policies require separation? What utilization is observed across the proposed pool? | Test workload compatibility, user experience, policy boundaries, and fully burdened cost with a representative cohort. |
| Persistent personal desktops | Does a user need a dedicated desktop whose state persists, and what specific workload or personalization requirement makes that necessary? | Validate the need for individual desktops rather than assuming every legacy user requires a static one-to-one replacement. |
| Windows 365 Cloud PCs | Does the workload and user experience fit a Cloud PC deployment, and what operating and policy requirements follow from that choice? | Pilot with representative users and compare observed utilization and fully burdened cost with the alternatives under consideration. |
The point of the comparison is not to rank these destinations in the abstract. Match each workload to its persistence, personalization, administration, policy, and user-experience needs, then validate cost using observed use rather than assumptions.
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- Classify workloads. Group users and applications by their actual requirements, dependencies, and policy needs. Identify workloads that no longer justify a virtual desktop as well as those that need dedicated or persistent treatment.
- Pilot cohorts in parallel. Test representative users on the proposed destination while the existing environment remains available. Check application behavior, access, policy enforcement, and day-to-day usability.
- Migrate department by department. Use pilot findings to refine the approach, then move cohorts in manageable stages rather than converting the entire estate at once.
- Reconcile costs against observed workloads. Compare the fully burdened cost of the target design with measured usage and operational needs; do not assume that a technically successful migration is automatically economical.
- Retire legacy infrastructure after operational parity. Confirm that migrated users can perform their required work and that support and policy operations are functioning before decommissioning the old environment.
What it means to treat an AI agent as an Entra identity
Manchester’s governance recommendation is to give each agent a distinct identity and appropriately bounded policies. Microsoft’s Agent ID documentation describes a specific implementation: an Entra agent identity is a special service principal created from a reusable agent identity blueprint. The agent identity itself has no credentials; the blueprint requests tokens on its behalf.
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A paired agent user account is a separate object, used when a resource requires a user object. It is not the same thing as the agent identity. That distinction matters when assigning permissions and evaluating which policies apply: an identity policy aimed at the agent does not automatically cover its paired user account.
Scope Conditional Access and other controls to the real access path
Microsoft documents Conditional Access support for agent identities, including policies that target agents and blueprints. Its scope is not universal: it covers the relevant Microsoft Entra token-acquisition flow. If an agent accesses a service outside that flow using an API key, that access can bypass Entra and the applicable Conditional Access policies. Microsoft also identifies security defaults as a condition under which these agent policies do not apply.
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Manchester’s interview mentions role-based access control (RBAC), Intune device compliance, and Microsoft Purview data loss prevention (DLP) as governance examples. Treat these as controls to configure and verify for the particular resource, device, identity, and data path—not as automatic protections every agent inherits. The interview does not establish that DLP blocks every possible route for data to leave an environment.
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- Credentials and permissions: Determine how tokens or other credentials are obtained, what resources the agent can access, and whether its permissions are limited to the task.
- Policy coverage: Check whether access uses the Entra token flow covered by Conditional Access and whether any paired user account needs separate policy treatment.
- Data and device paths: Verify that configured device and data controls apply to the actual route the agent uses; do not infer coverage from the presence of an identity alone.
Stage autonomy according to the risk of the task
Manchester proposes three stages for agent autonomy. This is his framework, not a Microsoft standard or formal certification scale. Use it to decide how much human review a task needs, and define permissions and escalation rules before increasing autonomy.
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- Human in the loop: The agent makes recommendations, but a person must approve changes before they are carried out.
- Human on the loop: The agent can perform bounded tasks while a person monitors its work and can intervene.
- Autonomous execution within policy boundaries: The agent acts without routine approval inside defined limits and escalates exceptions for human handling.
Plan for AI service costs without assuming a spending forecast
Manchester uses the phrase “tokenomic shock” for the challenge of managing multiple AI subscriptions and unpredictable usage across agents. The interview names OpenAI, Anthropic, and Microsoft Copilot as examples and compares the concern with early cloud-cost surprises. It supplies no spend dataset or independently measured estimate, so it does not quantify how large or widespread the effect is.
His forecast is that organizations will need more centralized management of AI costs and policies as use grows. For administrators, the practical implication is to track which services and agents are being used, who is accountable for them, and how usage maps to business tasks before treating anticipated cost controls as a settled market solution.
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