EBS Analysis: What is Microsoft Foundry Agent Service? – Microsoft Foundry

Microsoft Foundry Agent Service: A Managed Platform for Enterprise AI Agents

Enterprise organizations are under increasing pressure to operationalize artificial intelligence at scale, yet the journey from prototype to production-ready agentic systems remains fraught with complexity. Building custom orchestration, managing model lifecycles, ensuring secure tool access, and maintaining observability across distributed workloads demand specialized expertise and infrastructure. Microsoft Foundry Agent Service was engineered to address these exact pain points, offering a unified, managed platform that abstracts the undifferentiated heavy lifting of agent deployment while preserving the flexibility enterprises require for custom logic, data integration, and compliance. By providing a single entry point for model inference and tool orchestration, Foundry Agent Service enables teams to move from concept to deployed agent with confidence, governed by enterprise-grade controls and supported by a rich set of platform capabilities.

This article provides a comprehensive technical overview of Foundry Agent Service, examining its architecture, agent types, implementation pathways, security and governance model, operational considerations, and common pitfalls. The content is derived from official Microsoft Learn documentation and is presented as an original EBS consulting resource.

Architecture and Core Capabilities

At its foundation, Foundry Agent Service is a managed platform that decouples agent definition from infrastructure management. It supports a spectrum of build approaches, from fully declarative configuration to fully code-driven implementations, and serves as the cohesive layer between model selection, tool integration, and enterprise-scale deployment. The platform is designed to meet developers and architects wherever they are on the AI maturity spectrum, whether the goal is a quick internal tool or a customer-facing, mission-critical agent system.

Foundry Agent Service operates on the principle that agents act on the world through tools. The platform provides a curated toolbox of built-in capabilities—including web search, file search, code interpreter, and memory—while also enabling the incorporation of custom tools via functions, OpenAPI specifications, and Model-Context-Protocol (MCP) servers. This tooling model is central to the platform’s value: by centralizing tool definitions into a single, versioned, and governance-aware unit, Foundry ensures that any agent, regardless of its underlying framework, can reliably and securely access the data and services it needs.

The platform’s architecture is built around the Foundry project model. Each project acts as a logical boundary for resources, access controls, and observability data. Agents are defined within this context and interact with the platform through a single endpoint that handles model routing, tool invocation, and identity propagation. This design eliminates the need for developers to manage connection strings, authentication tokens, or model switching logic manually; the platform handles these concerns under the hood, allowing teams to focus on agent logic and business value.

Foundry also provides a model catalog that serves as the authoritative source for supported models. Agents can leverage any model listed in the catalog, including those from Microsoft, OpenAI, Anthropic, and other providers, subject to the project’s subscription and region. This catalog-driven approach ensures that model versions are tracked, that inference endpoints are consistently addressed, and that teams can swap models or upgrade versions with minimal rework.

Building Agents: Pathways and Agent Types

Foundry Agent Service categorizes agent construction into distinct types, each offering a different balance of control, management overhead, and customization. Understanding these types is essential for aligning the technology with specific use cases and organizational requirements.

Prompt agents represent the fastest path to a functioning agent. These are defined entirely through configuration—instruction prompts, model selection, and tool selection—without requiring application code or container management. Foundry executes the agent runtime on behalf of the user, providing immediate value for internal tools, prototyping, and production scenarios where custom orchestration is not a requirement. Portal-first and code-first modes are both supported: teams can author agents interactively in the Foundry portal, test them in the integrated playground, and then promote them to production, or they can define agents programmatically using SDKs or REST APIs, enabling integration with CI/CD pipelines, version control, and automated rollouts.

Voice-based prompt agents extend the managed agent paradigm into real-time spoken conversation. These agents are configured with a model, instructions, audio settings, optional greetings, and tools, and are made accessible through Voice Live, a managed real-time WebSocket-based interface that handles speech recognition, turn-taking, model interaction, and speech synthesis. Voice-based agents are particularly well-suited for customer-facing voice experiences, call-center scenarios, and any application requiring low-latency audio interaction without the team having to build or host the underlying voice orchestration infrastructure. It is important to note that voice-based agents and some related monitoring, evaluation, avatar, WebRTC, and telephony capabilities are currently in preview. Preview capabilities are provided without a service-level agreement and should not be deployed in production workloads until their support, security, compliance, and availability requirements have been thoroughly reviewed.

Voice-based agents support two primary model architectures. The first is optimized for natural, low-latency conversation, where the realtime model handles spoken input and output directly. Supported voice families and settings depend on the selected model and region. The second architecture provides broader text-model and Azure voice choices, offering explicit control over transcription, phrase lists, turn detection, and interim responses. While this second path introduces additional latency due to the staged processing of speech-to-text, model interaction, and text-to-speech, it enables greater flexibility in model selection and voice customization. A typical portal-created voice agent also relies on Azure Speech services for transcription or synthesis when required by the chosen architecture, and connects to telephony channels via Azure Communication Services or Twilio, with call routing managed through Azure Event Grid for Microsoft Teams Phone extensibility.

Hosted agents offer the greatest degree of control for teams that need to bring their own code, frameworks, or proprietary orchestration logic. Agents can be built using Agent Framework, LangGraph, the OpenAI Agents SDK, the Anthropic Agent SDK, the GitHub Copilot SDK, or custom code. The agent code is shipped as a container image or as a .zip source file (which Foundry builds into a container image automatically). Foundry then runs the agent as a managed endpoint, providing automatic scaling, a dedicated Microsoft Entra identity, session-level state persistence, and end-to-end observability. Under the hood, the agent code calls the Foundry project endpoint for model inference and tool orchestration, granting access to catalog models and platform tools while the team retains full ownership of the agent’s business logic.

Ephemeral agents, a fourth pathway, arise when developers call the Responses API directly from their own application code. In this model, the agent’s definition—instructions, tools, and model—is assembled within the calling process for each API call, and no persistent agent resource is created or managed within Foundry. This approach is advantageous when agent logic should version alongside the application codebase through source control and code review, when teams want to avoid the overhead of managing a separate Foundry resource, or when building embedded AI capabilities within existing applications. Despite the ephemeral nature, the Responses API still grants access to Foundry catalog models, platform tools, project-scoped data, On-Behalf-Of authentication, and project-level observability and governance.

Tool Integration and the Toolbox Framework

Tooling is a distinguishing factor in any agentic system, and Foundry Agent Service addresses this through its Toolbox framework. A toolbox is a curated, reusable grouping of tools that can be published once and consumed by any agent or runtime, regardless of the underlying framework. This centralization is a significant operational advantage: updates to tool configurations, authentication credentials, or versioning are made in a single location, and the change propagates to all agents that depend on the toolbox. Built-in tools cover common scenarios such as web search, file search within project data stores, code execution for data transformation, and conversational memory. Beyond the defaults, teams can register custom tools through Python functions, OpenAPI specification URLs, or remote MCP servers.

MCP servers enable agents to interact with external systems and services. Foundry supports remote MCP servers, including the Azure DevOps MCP Server, which can be connected to an organization to expose a subset of available tools to agents. Custom MCP servers can be hosted on Azure Functions and exposed via the /runtime/webhooks/mcp webhook endpoint, allowing organizations to expose proprietary APIs, automation workflows, or line-of-business systems to their agents. Authentication for MCP connections is governed by Microsoft Entra ID, with options including the agent’s managed identity, the project’s managed identity, OAuth On-Behalf-Of (OBO) passthrough, or unauthenticated access where the tool design permits. These authentication mechanisms ensure that agents can only perform actions within their authorized scope, and that credential rotation and permission changes are managed centrally.

The toolbox model also integrates with Foundry’s governance and compliance posture. By consolidating tool definitions, authentication configs, and version histories in one place, organizations can enforce standardized patterns across all deployed agents, reduce the attack surface, and simplify audits. Toolbox versioning is snapshotted automatically, and promoted versions can be defaulted or rolled back, providing a controlled evolution path for agent tooling.

Identity, Security, and Governance

Foundry Agent Service was architected from the ground up to satisfy enterprise security and governance requirements. Identity management is a first-class concern, with every agent capable of being provisioned a dedicated Microsoft Entra identity. This identity enables secure, scoped access to resources and APIs without the need to share secrets or embed credentials in code. Agents can use their managed identity to authenticate to external MCP servers, including those hosted on Azure Functions, and OBO passthrough is supported when the workflow requires propagating user or service principal identity through the agent’s actions.

Role-based access control is enforced through a combination of Microsoft Entra ID and Azure RBAC. Fine-grained permissions determine who can create, invoke, manage, and publish agents within a project. This model aligns with existing enterprise identity frameworks, reducing the learning curve for security teams and ensuring that agent deployment adheres to the organization’s broader access policies. Additionally, content safety features are integrated into the platform, with built-in filters designed to mitigate prompt injection risks—including cross-prompt injection attacks (XPIA)—and to prevent unsafe outputs from reaching end users or downstream systems.

Network isolation and data residency controls are also supported. Prompt agents can be run within the platform’s managed environment with network isolation options. Hosted agents support bring-your-own Azure Virtual Network (BYO VNet), where each agent session executes inside a VM-isolated sandbox connected to the customer’s virtual network. This capability is critical for organizations with strict data residency requirements, those that must keep data within a specific geographical or regulatory boundary, or those that need to integrate agents with on-premises systems through private links. When BYO VNet is used, the agent’s network traffic is confined to the customer’s network topology, and all inbound and outbound routing is governed by the customer’s network security groups and firewall rules.

Foundry also provides the ability to bring your own resources for conversation state, search, and storage. Teams can integrate their own Azure AI Search indexes, Azure Cosmos DB containers, or Azure Storage accounts to manage conversation history, vector search, or persistent data. This “bring your own resources” model ensures that sensitive data remains under the customer’s control and that compliance frameworks (such as GDPR, HIPAA, or ISO 27001) can be more easily satisfied, as the organization determines where data is stored, how it is accessed, and how long it is retained.

Responsible AI guidance is embedded throughout the platform. Microsoft’s broader responsible AI principles—fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability—inform the design of Foundry Agent Service features, from the content safety filters to the observability and evaluation tools. For organizations seeking a broader set of recommendations and governance resources, Microsoft provides a dedicated Responsible AI for Microsoft Foundry guidance document that outlines best practices, risk mitigation strategies, and compliance checkpoints.

Implementation Considerations and Prerequisites

Successfully implementing Foundry Agent Service requires attention to several prerequisites and architectural considerations. At the foundational level, an organization must have a Microsoft Foundry resource provisioned, which serves as the container for projects, agents, models, and tooling. Access to the resource is governed through Microsoft Entra ID, and users must have appropriate roles assigned to create agents, manage toolboxes, and configure networking.

For teams opting for hosted agents, the development environment must support containerization. Agents can be authored in Python, JavaScript/TypeScript, or other runtimes supported by container runtimes. The agent code must define a entry point that the Foundry runtime can invoke for model inference and tool orchestration. When bringing a .zip source file, Foundry automatically builds a container image, but teams should ensure that the source code adheres to the expected contract for tool registration, session state management, and error handling. For custom container images, the Dockerfile must expose the necessary ports and environment variables as specified in the Foundry documentation.

Network configuration is a critical implementation step, especially for organizations using BYO VNet with hosted agents. The Azure virtual network must have sufficient subnet address space, and inbound/outbound rules must allow the necessary traffic to and from the Foundry service endpoints. Additionally, private DNS zones may be required to resolve Foundry service URLs within the customer’s network. Teams should also plan for outbound internet access if their agents rely on tools that require external connectivity, such as web search or third-party MCP servers.

Tool integration demands careful attention to authentication and scope. When connecting MCP servers, the principle of least privilege should be applied: only the tools and actions necessary for the agent’s function should be exposed. Authentication mechanisms—whether Entra-managed identity, OBO passthrough, or unauthenticated access—must be tested thoroughly in the playground before promotion to production. Teams should also review the toolbox versioning strategy, deciding how new tool versions will be promoted, tested, and rolled back across agent deployments.

For voice-based agents, additional considerations apply. The choice of voice architecture influences model availability, latency, and cost. Teams should verify that the selected model and voice family are available in their Foundry resource region and that preview features are enabled if required. Audio input and output devices, telephony channel connections (Teams Phone or Twilio), and Azure Speech service configuration must be validated. Cost modeling should account for generative AI model input and output tokens, speech recognition and synthesis charges, custom voice training and hosting fees (if applicable), Application Insights ingestion and retention, and connected telephony services. While specific pricing data is not provided here, the platform’s pricing documentation outlines the charge categories for voice-based agent workloads.

Observability, Evaluation, and the Development Lifecycle

Foundry Agent Service supports a full build-test-deploy-monitor workflow that is baked into the platform experience. The development lifecycle encompasses several stages, each designed to reduce risk and improve agent quality before production deployment.

During the create phase, agents are defined either through the portal’s interactive interface or programmatically via SDKs and REST APIs. The portal-first approach allows rapid iteration: an agent can be built, tested in the playground, and refined through a visual interface before any code is written. The code-first approach, by contrast, integrates agent definition into the team’s source control and deployment pipeline, enabling version-controlled changes, code review, and automated testing as part of CI/CD.

Testing is facilitated through the agents playground, a web-based interface where developers can chat with their agent, inspect tool invocations, and validate behavior. MCP server integrations, including custom Azure Functions-hosted servers, can be exercised directly in the playground to verify tool connectivity, permissions, and expected outputs. This inline testing capability reduces the feedback loop and helps catch configuration errors early.

Tracing is provided out of the box, giving teams visibility into every model call, tool invocation, and decision point within an agent execution. Traces include input prompts, model responses, tool parameters, returned data, and timing information. This granular observability is essential for debugging complex agent behaviors, understanding cost drivers, and identifying performance bottlenecks. Trace data can be exported to Azure Application Insights for long-term retention, dashboarding, and correlation with other telemetry.

Evaluation is a key differentiator. Foundry provides tools to run systematic evaluations against agent versions, using predefined or custom metrics to measure quality, correctness, latency, and tool usage. Evaluations can be executed against historical data, simulated user queries, or automated test suites. The platform’s agent optimizer can automatically refine agent instructions based on evaluation results, suggesting prompt adjustments that improve performance. This closed-loop feedback mechanism enables continuous improvement without manual prompt engineering drudgery.

Publish promotes an agent from a development resource to a managed endpoint with a stable URL. Published agents inherit the identity, access controls, and toolbox configuration of the source project. Distribution capabilities extend beyond simple endpoint invocation: Foundry Agent Service supports the OpenResponses and Activity Protocols for Microsoft 365 publishing, an Invocations protocol for flexible endpoint integration with custom applications and services, and the A2A (Agent-to-Agent) protocol for agent-to-agent communication. A2A v1.0 is generally available, while v0.3 remains in preview, enabling scenarios such as multi-agent workflows, coordinated task execution, and interoperability with third-party agent frameworks.

Monitoring continues after publication. Service metrics and dashboards provide real-time visibility into agent invocation rates, error rates, latency percentiles, and tool usage patterns. Integration with Azure Monitor and Application Insights ensures that agents are tracked alongside the rest of the organization’s cloud workloads, and alerts can be configured to notify operations teams of anomalies or SLA deviations.

Operational Considerations, Quotas, and Limitations

Operationalizing Foundry Agent Service at enterprise scale requires awareness of the platform’s quotas, limits, and regional support model. While the documentation specifies that quotas and limits vary by resource region, subscription tier, and enabled preview features, general categories include per-project agent counts, per-agent invocation rates, token throughput limits, and MCP server connection caps. Teams should consult the Foundry portal or their account team for the exact quota values applicable to their environment, and should design their agent architectures to gracefully handle quota enforcement, such as by implementing retry logic, request throttling, or multi-agent distribution.

Regional availability is another important constraint. Model availability, voice architecture support, and preview feature enablement are all dependent on the Foundry resource’s selected region. Before committing to a specific model or architecture, teams should validate that the desired capabilities are supported in their region. The Foundry portal provides region-specific model and feature lists, and early engagement with Microsoft account teams can help accelerate the onboarding of preview features if required.

Cost management is an ongoing operational concern. Beyond the generative AI model costs, voice-based agents incur charges for speech recognition and synthesis, Azure Communication Services or Twilio telephony minutes, Azure Event Grid messaging, Application Insights data ingestion and retention, and any custom tool usage (such as third-party API calls). Teams are encouraged to establish tagging strategies, budget alerts, and consumption reviews to keep agent-related spending within organizational limits. The platform’s cost transparency, combined with the trace and evaluation data, makes it possible to attribute costs to specific agents, teams, or business functions.

Limitations also exist in the preview space. Voice-based agents, avatar integration, WebRTC-based real-time media processing, and certain telephony extensibility features are marked as preview and come without service-level agreements. Organizations planning production deployments should thoroughly evaluate these capabilities against their availability, disaster recovery, and compliance requirements before adoption. Additionally, while the Responses API provides a robust pathway for ephemeral agent usage, it does not support the persistent versioning, publishing, or distribution features that are available to hosted or prompt agents. Teams requiring long-term agent lifecycle management should opt for the persistent agent types.

Why This Matters to Enterprise IT

For enterprise IT leaders, Foundry Agent Service represents a strategic shift in how AI capabilities are consumed, governed, and scaled. Traditional approaches to building AI agents often require stitching together disparate components: a model hosting service, a tool integration layer, an orchestration framework, a security boundary, and an observability stack. Each of these pieces introduces integration risk, operational overhead, and potential compliance gaps. Foundry Agent Service consolidates these layers into a single, managed service, delivering a predictable runway for AI experimentation while maintaining the rigor expected of enterprise IT.

From a risk and compliance perspective, the platform’s built-in identity model, network isolation options, and data residency controls provide a clear pathway to meet regulatory requirements without the need for custom security plumbing. The integration with Microsoft Entra ID and Azure RBAC means that existing identity governance policies can be extended to agents, ensuring that access is granted on a just-in-time, just-enough basis. For organizations operating in highly regulated industries such as finance, healthcare, or government, the ability to bring your own resources and run agents within customer-controlled virtual networks is a significant enabler of compliance.

Operationally, the full lifecycle support—create, test, trace, evaluate, optimize, publish, monitor—embeds best practices into the developer experience, reducing the likelihood of production incidents caused by untested prompts, uncontrolled tool access, or unmonitored resource consumption. The platform’s versioning and rollback capabilities provide a safety net for iterative development, while the evaluation and optimization tools help teams achieve higher agent quality with fewer manual cycles. For IT organizations that have struggled with the “last mile” of AI adoption—getting prototypes into secure, governed production—Foundry Agent Service offers a concrete and supported path forward.

Finally, the interoperability protocols (OpenResponses, Activity Protocols for Microsoft 365, Invocations, A2A) future-proof the investment by ensuring that deployed agents can evolve with the broader ecosystem. As the industry moves toward standardized agent communication and integration patterns, enterprises deploying via Foundry will be positioned to interoperate with copilots, teammate agents, and external agent networks without re-architecting their core logic.

EBS Consulting Perspective

From a consulting standpoint, Foundry Agent Service is most compelling for organizations that already inhabit the Microsoft ecosystem and are seeking to accelerate AI agent adoption without incurring the full cost of building and maintaining a custom agent platform. The service’s tight integration with Microsoft Entra, Azure RBAC, and the broader Foundry resource model means that security and governance are not afterthoughts but foundational layers. For enterprises with strict data residency or compliance requirements, the BYO VNet support and bring-your-own-resources model provide the necessary control boundaries to meet auditors’ expectations while still enjoying the productivity gains of agentic AI.

However, the consulting team at EBS also observes that the preview status of voice-based agents and some telemetry capabilities means that production readiness depends heavily on the specific use case. Organizations should treat voice-first deployments as experimental until the relevant features graduate from preview and receive service-level agreements. Additionally, the toolbox model, while powerful, requires disciplined governance: without clear policies on tool onboarding, version promotion, and access scoping, the centralization benefit can devolving into a governance bottleneck. EBS recommends that early adopters establish a toolbox advisory board—representatives from security, architecture, and line-of-business teams—to define and enforce tooling standards across the enterprise.

For teams considering the hosted agent path, the consulting perspective emphasizes the importance of upfront investment in containerization patterns, CI/CD pipelines, and identity configuration. While Foundry manages the runtime scaling and endpoint exposure, the agent logic itself remains the team’s responsibility. Organizations that underinvest in these foundational practices often face challenges when scaling agents beyond the prototype stage. EBS advises a phased approach: start with prompt agents or ephemeral Responses API usage to validate use case feasibility and organizational demand, then graduate to hosted agents as the team matures its DevOps and AI engineering competencies.

Finally, the interoperability protocols warrant serious consideration for long-term strategy. Enterprises that standardize on OpenResponses or A2A early will find it easier to integrate with future copilot platforms, third-party agent marketplaces, and internal agent ecosystems. EBS suggests that IT architects map out potential integration points—such as Microsoft 365 copilot extensibility, Teams message extensions, or external agent marketplaces—and evaluate how Foundry’s protocol support aligns with those roadmaps. This forward-looking perspective ensures that the initial investment in Foundry Agent Service continues to deliver value as the AI landscape evolves.

Practical Next Steps

For organizations ready to explore Foundry Agent Service, the following practical steps can accelerate value realization while minimizing risk:

1. Assess the build pathway. Determine which agent type best matches the intended use case: prompt agents for rapid internal tools, voice-based prompt agents for customer-facing voice interactions (with preview awareness), or hosted agents for custom orchestration and proprietary code. Match the agent type to the team’s current skill set and long-term strategic goals.
2. Provision a Foundry resource and project. Establish a Microsoft Foundry resource within the appropriate subscription and region, then create a dedicated project to isolate agents, toolboxes, and access controls. Assign Microsoft Entra roles that align with the principle of least privilege.
3. Experiment in the playground. Use the Foundry portal’s agents playground to prototype the chosen agent type. Test tool integrations, validate model behavior, and familiarize the team with tracing and evaluation capabilities. For voice agents, validate the selected architecture and voice family against the resource region.
4. Define and publish a toolbox. Curate the set of tools that agents will need, register them in a toolbox, and configure authentication scopes. Promote the toolbox to default once the team has validated tool behavior across multiple agent prototypes.
5. Implement versioning and governance from the start. Take advantage of automatic version snapshotting and publishing workflows. Establish a change management process that includes evaluation, stakeholder review, and rollback readiness before any agent promotion to production.
6. Integrate observability and cost monitoring. Configure Azure Application Insights or Azure Monitor to receive trace and metric data from the agent service. Set up budget alerts and consumption reviews to keep agent-related spending transparent and accountable.
7. Plan for interoperability. Evaluate which protocol(s)—OpenResponses, Activity Protocols for Microsoft 365, Invocations, or A2A—align with the organization’s integration roadmap. Early adoption of these standards will reduce future rework as the agent ecosystem expands.

By following these steps, enterprises can move from curiosity to production with a clear, governed, and scalable agent platform that aligns with their broader IT and AI strategies.

Conclusion

Microsoft Foundry Agent Service is more than a collection of tools and APIs; it is a strategically designed platform that addresses the real-world complexities of building, deploying, and scaling AI agents at enterprise scale. By unifying model access, tool integration, identity management, security controls, and lifecycle observability under a single managed umbrella, Foundry removes the undifferentiated heavy lifting that has historically slowed AI adoption in large organizations. The service’s tiered approach—from configuration-only prompt agents to fully code-driven hosted agents—ensures that teams can start where they are and evolve their capabilities maturally, without forced re-architectures or vendor lock-in.

For enterprise IT leaders, the decision to adopt Foundry Agent Service should be framed not just as a technology purchase, but as a strategic enabler of AI governance, compliance, and operational efficiency. The platform’s alignment with Microsoft’s identity and compliance frameworks, its support for data residency and network isolation, and its built-in lifecycle management capabilities make it a compelling choice for organizations that must balance innovation with risk mitigation. Meanwhile, the interoperability protocols and distribution capabilities future-proof the investment, ensuring that deployed agents can adapt to emerging ecosystems and usage patterns.

EBS consulting recommends that organizations approach Foundry Agent Service with a clear use-case-driven strategy, starting with a pilot that validates the chosen agent type, tooling, and governance model before scaling across the enterprise. Leverage the playground and evaluation tools to iterate rapidly, establish toolbox standards early, and integrate observability from day one. As the preview features graduate and the protocol ecosystem matures, the strategic value of Foundry Agent Service will only increase, positioning early adopters to lead in the next wave of agentic AI innovation.

Now is the time to evaluate how Foundry Agent Service can fit into your organization’s AI roadmap. Engage with your Microsoft account team, provision a trial resource, and begin prototyping today—the platform’s managed capabilities and enterprise-grade controls are designed to help you deliver trusted AI agents at speed.

EBS Consulting Advice

If your organization is evaluating What is Microsoft Foundry Agent Service? – Microsoft Foundry, do not treat the technology decision in isolation. Start with the business outcome, current architecture, security and identity controls, operational constraints, migration dependencies and governance requirements. A practical assessment should identify the current-state gaps, prioritize the risks and define an implementation roadmap with measurable outcomes.

EBS can help assess the environment, develop the architecture and modernization roadmap, and translate the technical options into an actionable business plan. Relevant EBS services: Microsoft Azure consulting Microsoft Solution Assessments Modern Workplace.

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