Training & Learning Paths – Request for Guidance and Walkthrough
Jumeirah Hospitality Group is looking to develop structured training and learning paths for our teams. We would like to understand the end-to-end process, including: How to design and structure learning paths How to upload and organize content How to assign paths to different roles or departments How to track completion and performance We would appreciate it if someone could walk us through the process and provide clear guidance on the steps involved. Please let us know who we should contact and when availability can be arranged.New AI gateway capabilities in Azure API Management
Multi-model, multi-protocol AI applications are quickly becoming the norm. Teams are mixing OpenAI, Anthropic, and Vertex AI models, exposing tools through MCP, and wiring agents together with A2A. As that surface grows, so does the work of keeping it secure, observable, and consistent. Our ongoing strategy for the AI gateway capabilities in Azure API Management centers on that problem: providing one place to manage models, MCP tools, and agents, no matter which provider or protocol is behind them. The updates below are the latest steps in that direction. Unified Model API (preview) The headline change in this release: the Unified Model API lets clients speak one API format — OpenAI Chat Completions — while API Management transforms requests to the backend provider, whether that's a model using OpenAI Chat Completions or Anthropic Messages API. By centralizing model access behind a single API layer, you can: Standardize on a single API format for clients, independently from the formats used by backend models. Unify observability, security, and governance with policies that apply across model providers. Configure failover across model providers. Decouple client-facing model names from backend model names using aliases. Learn more about the unified model API. Model aliases Model aliases give clients a stable, provider-neutral name to use when calling a model. By assigning an alias like gpt or claude-sonnet, you decouple the client-facing model name from the actual backend deployment. That makes a few common operations a lot easier: Upgrading a model. Update the alias target to point at a new version — no client code changes required. A/B tests. Shift traffic between backends behind the same alias using API Management's load balancing capabilities. Vendor swaps. Replace one provider with another without touching application code. Model discovery Developers can discover available models by calling the /models endpoint of the Unified Model API. API Management returns the list of model aliases, so apps and tools can adapt to what the platform team has published — without out-of-band documentation. Anthropic and Vertex AI models (GA) AI gateway policies and observability now work with Anthropic and Google Vertex AI models, alongside the providers we already support. You can: Apply runtime policies such as content safety, token limits, and semantic caching to Anthropic and Vertex AI traffic. Collect logs, traces, and metrics for these models in the same place as the rest of your AI traffic. If you're running a multi-provider setup, you no longer need a separate governance story for each vendor. Learn more about AI gateway capabilities in API Management. Anthropic API operations in Microsoft Foundry import When you import a Microsoft Foundry resource as an API in Azure API Management, the import now creates operations for Anthropic APIs alongside the existing model APIs. In a few clicks, you can stand up an API that mediates traffic to Foundry models using either the OpenAI or Anthropic API format — no manual operation definitions needed — and then apply the same policies, security, and observability you use for the rest of your AI traffic. Learn more about Microsoft Foundry import. Token metrics for additional token types (preview) Token tracking used to stop at prompt, completion, and total tokens. Modern models add cached, reasoning, and thinking tokens, which can make up a significant share of token consumption, cost, and latency. API Management now logs metrics for these additional token types into Application Insights, across API formats (OpenAI Chat Completions, OpenAI Responses, and Anthropic Messages API) and providers (Microsoft Foundry, OpenAI, Amazon Bedrock, Google Vertex AI, and others). With richer signals, your cost dashboards, budget alerts, and capacity planning can actually reflect how today's models behave. Learn more about token metrics. Content safety for MCP and A2A (GA) The llm-content-safety policy now covers MCP and A2A traffic in addition to LLM traffic. That includes MCP tool-call arguments, MCP response text, and A2A payloads. A couple of related improvements: llm-content-safety can now be configured directly as an outbound policy. Two new attributes — window-size and window-overlap-size — let you tune how messages exceeding the Azure Content Safety limit of 10,000 characters are chunked and forwarded for validation, balancing detection sensitivity with Azure Content Safety call volume. The result is one consistent safety policy across LLM, MCP, and A2A flows instead of stitching together custom filters per protocol. Learn more about the content safety policy. A2A APIs (GA) Support for Agent-to-Agent (A2A) APIs in API Management is now generally available. Agent APIs can now be governed with the same policies, identity, and observability you use for the rest of your APIs. What you can do with A2A APIs in API Management: Mediate JSON-RPC runtime operations to your agent backend with full policy support — including the content safety improvements above. Expose and manage agent cards, automatically transformed by API Management to represent the managed agent API. Log traces to Application Insights using OpenTelemetry GenAI semantic conventions for deep correlation between API and agent execution traces. What's new in GA, on top of the preview: Available in classic tiers, in addition to v2 tiers — bring A2A governance to existing API Management resources without migrating tiers. Richer diagnostic logging for A2A APIs, giving more actionable telemetry for monitoring and troubleshooting agent traffic. Learn more about A2A support in API Management. Related: Bring Your Own Model in Foundry Agent Service (GA) Last month, Bring Your Own Model (BYOM) in Foundry Agent Service went GA. BYOM lets enterprise teams route Foundry agent model calls through their own infrastructure — typically for compliance, governance, or to reuse an existing model gateway. This pairs naturally with the AI gateway capabilities in Azure API Management. Put API Management in front of your models, apply the policies and observability described above, and have Foundry agents call through it — getting consistent governance for both your direct AI traffic and your agent workloads. Get started Together, these updates make Azure API Management a more complete AI gateway: consistent governance, security, and observability across models from various providers, MCP tools, and agent interactions. Some of these features are still rolling out. They will first become available in v2 tiers of API Management and in the AI release channel for classic tiers, then continue rolling out to the rest of classic tier resources over the following weeks. Get started with the unified model API or explore the AI gateway capabilities in API Management.
MCP Test Console and Git Repository synch in Azure API Center
Why This Matters As organizations race to build AI-powered applications, the Model Context Protocol (MCP) has emerged as the standard way to connect AI agents with external tools and data sources. Managing these MCP servers at enterprise scale, however, has been a growing challenge — until now. AI agents are only as useful as the tools they can access. MCP servers expose those tools — from databases and internal APIs to third-party services — in a standardized way that any AI agent or model can consume. As your MCP ecosystem grows, so does the challenge of keeping track of what's available, what's working, and what your teams are actually using. Azure API Center already serves as a centralized registry for APIs across your organization. Now it extends that same governance model to MCP servers, complete with developer-friendly discovery, live testing, and automated synchronization from your source repositories. New Feature: MCP Test Console in the API Center Portal Developers can now test MCP server tools interactively without leaving the Azure portal. Once an MCP server is registered in your API Center inventory, the API Center portal — your organization's customizable developer portal — surfaces a dedicated test console on the server's Documentation tab. Developers simply select a tool, click Run tool, and immediately see the response. This means your teams can: Validate tools before connecting them to agents — no more building a test harness from scratch. Explore tool schemas interactively — the portal surfaces endpoint details and input/output schemas alongside the live console. Onboard faster — developers browsing your internal MCP registry can go from discovery to verified integration in minutes. The MCP server tiles in the portal provide a clear, browsable view of all registered servers. Each tile surfaces the server's endpoint URL, available tools, and installation instructions for Visual Studio Code — giving developers everything they need to get started in one place. Getting started: Set up your API Center portal, then navigate to any registered MCP server. On the Documentation tab, select a tool and click Run tool to open the test console. New Feature: Synch MCP Servers from a Git Repository Managing API assets shouldn't require manual registration every time something changes. With Git repository integration, Azure API Center can automatically sync assets — including MCP server definitions — directly from your source repository. How It Works When you connect a Git repository to your API Center: An environment is created in your API Center representing the repository as an asset source. API Center regularly synchronizes MCP servers from the repository into your inventory — no manual intervention required. Assets appear in your inventory on the Inventory > Assets page with a visual link indicator, making it easy to identify which assets are source-controlled. This is especially valuable for teams that maintain MCP server definitions, skill files, or OpenAPI specs in version control. As your repository evolves, your API Center inventory stays current automatically. Setting It Up Step 1: Secure your access credentials (for private repos) If your repository is private, store a personal access token (PAT) as a secret in Azure Key Vault. Your API Center instance uses a managed identity to retrieve this secret securely — you can configure the managed identity manually or let API Center handle it automatically during the integration setup. Step 2: Connect the repository In the Azure portal, go to your API Center and navigate to Platforms > Integrations > + New integration > From Git repository. You'll configure: Repository URL — including an optional branch and subfolder path (e.g., https://github.com/<org>/<repo>/tree/main/skills). Git provider — such as GitHub. Asset type configuration — API Center defaults to a skill asset type with the file pattern **/skill.md, but you can add additional asset types to match your repository structure. PAT reference — select the Key Vault secret containing your PAT, if applicable. Environment details — give the repository environment a friendly name, resource ID, type (e.g., Production), and lifecycle stage for synced assets. Step 3: Let the sync run Once created, the integration runs automatically. Your assets will appear in the Inventory > Assets view, linked to their source in the repository. Access Control for Private Repositories The integration uses Azure's managed identity framework to authenticate to Key Vault. Assign your API Center's managed identity the Key Vault Secrets User role on your Key Vault to grant the necessary read access. If you prefer, API Center can configure this automatically — just enable the Automatically configure managed identity and assign permissions option during integration setup. Bringing It Together: A Complete MCP Governance Story Together, these two features complete an end-to-end workflow for enterprise MCP governance: Register → Connect your Git repository and let API Center automatically synch your MCP servers and skills as they evolve. Discover → Developers and AI engineers browse the API Center portal to find the right MCP server for their agent, with full schema visibility and endpoint details. Test → The built-in test console lets developers validate tools interactively before committing to an integration. Govern → Use API Center's access management capabilities to control who can view and consume specific MCP servers across your organization. And if you're building MCP servers on Azure services, the registry integrates directly with Azure API Management, Azure Logic Apps, and Azure Functions — so your MCP ecosystem and your API ecosystem share a single source of truth. Get Started Register and discover MCP servers in Azure API Center Synchronize API assets from a Git repository Set up the API Center portal Explore MCP Center — Azure API Center's public MCP registryMore Control, Less Overhead: Custom Domain Upgrades in Azure API Management v2
Multiple custom domains in Premium v2 Large organizations rarely expose APIs under a single domain. A global enterprise might need api.contoso.com for external partners, apis.hrportal.contoso.com for internal teams, and dev.europe.contoso.com for a regional developer portal — all at once. Until now, achieving this required spinning up separate API Management instances, adding cost and operational complexity. Azure API Management Premium v2 now supports multiple custom domains within a single instance — across gateway, developer portal, and management endpoints. This allows organizations to: Configure distinct hostnames for different endpoints and target audiences Align API experiences with business units, products, or regional brands Simplify domain-scoped networking and security policies Reduce the need for separate APIM instances created solely for domain separation For enterprises managing large, distributed API estates, this provides greater flexibility in how APIs and developer experiences are exposed — while maintaining centralized governance. Wildcard custom hostnames in Premium v2 and Standard v2 As API estates grow, managing individual certificates for every subdomain becomes a scaling problem fast. Each new surface — payments.api.contoso.com, inventory.api.contoso.com, orders.api.contoso.com — previously required its own hostname registration and certificate. Ten new API surfaces meant ten separate management tasks. Azure API Management Premium v2 and Standard v2 now support wildcard entries in custom hostnames. A single *.api.contoso.com entry paired with a single wildcard certificate covers all subdomains automatically — no per-subdomain configuration required. This helps teams: Simplify certificate and domain management at scale Accelerate onboarding of new API surfaces without repeated hostname setup Maintain consistent branded endpoints across dynamic subdomains Reduce operational overhead for rapidly growing API environments By extending this capability to both Premium v2 and Standard v2, Azure API Management makes flexible, scalable domain management accessible to more organizations without requiring higher-tier deployments. Both updates are generally available now. Learn more about Azure API Management v2 tiers and how they help organizations build scalable, enterprise-grade API platforms. Further reading: Configure a custom domain name for Azure API ManagementAzure API Center Introduces a Data Plane MCP Server for Enterprise-Wide API and AI Asset Discovery
As organizations scale their adoption of MCP-based tooling and AI agents, one challenge keeps surfacing: developers spend too much time figuring out what APIs, tools, and AI assets exist — and then manually wiring up connections to each one. Today, we're excited to announce general availability of a new capability that changes that. What's new Azure API Center now provides a data plane MCP server — a unified enterprise discovery endpoint that gives agents and developer tools a single connection point to your organization's full catalog of registered MCP servers, tools, APIs, and AI assets. Instead of hunting across systems or hand-configuring integrations one by one, developers and agents can now connect once and immediately access everything that's been registered in your API Center. Why this matters The MCP ecosystem is growing fast. So is the number of enterprise APIs and AI assets that teams need to manage and consume. Without a central discovery mechanism, that growth creates friction — more manual configuration, more drift between what's available and what's actually reachable, and more integration complexity for every new agentic application. The Azure API Center data plane MCP server addresses this directly. With it, teams can: Give agents centralized access to enterprise APIs and AI assets without custom routing logic Eliminate manual configuration of connections to individual MCP servers Automatically surface newly registered MCP servers and tools without reconfiguration Simplify discovery and consumption across a rapidly growing enterprise catalog Built for how organizations actually operate Agentic applications don't just need APIs — they need to find the right APIs, trust that the catalog is current, and connect reliably at scale. By acting as a unified discovery endpoint, Azure API Center helps teams operationalize AI ecosystems with stronger discoverability, governance, and developer productivity, while meaningfully reducing integration complexity. This is especially valuable as enterprises move from experimenting with AI agents to deploying them in production workflows, where manual integration approaches don't scale. How to enable the data plane MCP server Turning on the MCP server takes just a few clicks in the Azure portal. Navigate to your API Center instance and open Data API settings under the Consumption section in the left-hand menu. From there, under MCP endpoint, toggle Enable API Center MCP endpoint to on. Once enabled, your MCP endpoint URL (in the form https://<your-instance>.data.<region>.azure-apicenter.ms/mcp) will appear and can be copied directly for use in agent configurations or developer tools. Note: When enabled, the MCP endpoint is also surfaced on the developer portal homepage, so developers can connect via CLI without needing to look up the URL separately. You can also enable the Plugin marketplace endpoint from the same settings page to let developers browse and install approved plugins and skills from your organization's marketplace. The Visibility section lets you control which APIs are exposed through the data plane — use Add condition to filter the catalog based on your governance requirements. Get started Learn more about Azure API Center and how organizations are building unified catalogs for APIs, MCP tools, agents, and AI assets.Find what you need, faster: Azure API Center now supports custom metadata filtering
Enterprise API and AI catalogs have expanded dramatically. Where teams once managed dozens of APIs, they now govern hundreds — spanning business units, environments, compliance domains, and an ever-growing roster of AI assets. The catalog itself has become a discovery challenge. What's new Developers can now filter catalog assets using organization-defined metadata attributes. These aren't generic tags — they're the classifications your organization already uses: environments, business units, domains, compliance tiers, ownership groups, and more. Custom metadata filtering works across all major asset types in Azure API Center: APIs Skills Agents MCP tools Why it matters Discovery friction is a hidden tax on developer productivity. When a developer needs to find the right API for a project, every minute spent navigating inconsistent lists or applying manual filters is a minute not spent building. At scale, this compounds quickly. Custom metadata filtering addresses this directly by aligning the catalog's search experience with how your organization already thinks about its assets: Surface the right assets faster — filter by internal classifications and governance models instead of browsing overwhelming lists Improve discoverability at scale — no need to retag or reorganize existing assets to make them findable Align with your organizational taxonomy — filter by domain, environment, business unit, compliance requirement, or any custom attribute your teams already use Built for governed, AI-ready teams This update reinforces Azure API Center's role as the foundation for scalable, AI-ready discovery experiences — where governance and developer velocity move together, not against each other. By making enterprise catalogs easier to navigate, Azure API Center helps developers spend less time searching and more time building with governed APIs and AI assets. Get started Learn more about Azure API Center custom metadata filtering and how organizations are building scalable, AI-ready discovery experiences.Built-in gateway support for workspaces in Azure API Management
Workspaces in Azure API Management let platform teams hand off API ownership to individual API teams while keeping centralized governance. Until now, using them meant deploying a dedicated workspace gateway on the Premium tier — adding cost, operational overhead, and limiting regional availability. That requirement is going away. Workspaces can now be associated directly with the built-in gateway, and this capability is generally available. What changes Available in more tiers. Use workspaces on the built-in gateway in any API Management tier except Consumption. Available in every region. Create workspaces in any region where API Management is supported. All built-in gateway capabilities apply. Workspaces deployed with the built-in gateway inherit features that dedicated workspace gateways don't offer today, including multi-region deployments, custom hostnames, and Private Link connectivity. Availability Rolling out now to v2 tiers, with Azure portal UI support expected around July. Rollout to classic tiers (Developer, Basic, Standard, Premium) will begin by August and may take up to a few months to complete. Get started Learn more about workspaces and how to deploy them on the built-in gateway.GA: Azure API Center Now Supports Plugin Registration
As organizations scale their AI and integration ecosystems, one challenge keeps surfacing: developers don't have a reliable, governed way to discover and reuse the plugins their teams have already built. Plugins end up siloed in individual repos, shared over Slack, or duplicated across teams — slowing down development and creating governance blind spots. What's new With this update, developers can register plugins directly into Azure API Center's enterprise catalog. Once registered, plugins are discoverable, governable, and consumable alongside the rest of an organization's API and AI portfolio — no more hunting across repositories or relying on word-of-mouth to find what's already been built. Why it matters Plugins are increasingly central to how AI-powered applications are built. Agents depend on them. Integrations are built on top of them. But without a governed home, even well-built plugins go undiscovered and get rebuilt from scratch. Plugin registration in Azure API Center helps organizations: Centralize plugin discovery within a governed catalog, so developers always know where to look Surface vetted plugins that teams can confidently find and reuse — reducing duplication and accelerating development Align plugins with source-controlled workflows, keeping development practices consistent across the catalog Reduce friction between building a plugin and enabling it for real-world integration One catalog for your entire AI ecosystem This update reflects a broader vision for Azure API Center: a single, unified catalog for everything an organization builds and consumes — APIs, plugins, agents, and AI assets. By bringing plugins into this experience, teams can operationalize reusable integration and AI capabilities at scale, with the governance and discoverability that enterprise development requires. Whether your teams are building copilot extensions, orchestration layers, or custom integrations, Azure API Center gives them a governed foundation to build on — and a shared place to discover what's already there. Learn more about Azure API Center and how organizations are building centralized catalogs for APIs, plugins, agents, and AI assets.Azure API Center now supports agent registration, agent assessment, and Git-based synchronization
What's new Three capabilities are now generally available: Capability What it does Agent registration Register agents directly into the enterprise catalog for cross-team discovery and reuse. Agent assessment LLM-as-a-Judge framework scores agents across 6 criteria before catalog registration. Git synchronization Connect a repo and keep agent definitions automatically in sync with source control. Agent assessment — six weighted criteria, automatically enforced When assessment is enabled, every agent is scored on creation or update. Up to 8 criteria are supported; weights are fully configurable by platform teams. Criterion Weight What it evaluates capability-transparency 0.15 Documents tools, delegated capabilities, external systems, access levels, and capability boundaries. composition-resource-discipline 0.15 Named skill/sub-agent references, invoke guidance, no inline duplication, resource/token constraints. operational-protocol-quality 0.25 Structured workflow with named steps, decision points, failure modes, recovery paths, and pre-flight checks. output-contract 0.10 Specifies output format, mandatory sections, evidence requirements, confidence semantics, and dead-end handling. purpose-scope-clarity 0.15 Clear role identity, type (specialist/orchestrator), activation triggers, anti-triggers, and refusal behavior. safety-consent-architecture 0.20 Classifies risk, distinguishes idempotent vs approval-required actions, enumerates NEVER/ALWAYS rules, documents failure-mode safety. Git-based synchronization — connect once, stay in sync automatically Integrating a Git repository creates an environment in API Center representing the repo as an asset source. API Center polls for changes and reflects them in Inventory > Assets — linked assets display a provenance icon. Agent definitions live in source control and evolve continuously — yet keeping a catalog manually in sync with a codebase is slow, error-prone work that no platform team should have to do. Git-based synchronization connects Azure API Center directly to your repository, polling for changes and reflecting them in the inventory automatically so the catalog always represents the current state of your agents. Linked assets carry a provenance icon that traces them back to their source repository, giving developers immediate visibility into where an agent comes from and whether it is actively maintained. Because assessment gates run before any version is promoted from the repo into the catalog, only agents that meet your organization's quality criteria ever reach developers — enforcing governance at the point of commit, not as an afterthought. A single integration supports multiple asset types through configurable file patterns, so teams can sync agents, skills, and APIs from one repository with per-type control and no duplication. A2A agent sync from Azure API Management — publish once, discover everywhere Azure API Management → continuous sync → Azure API Center One-way · updates within minutes · includes API definitions, environments & deployments As teams publish more A2A agents through Azure API Management, manually registering each one into a discovery catalog creates friction that slows developer productivity and risks catalog staleness. Azure API Center now automatically synchronizes A2A agents — alongside APIs and MCP servers — published in an API Management instance, so every agent that reaches runtime is immediately visible in the centralized catalog without any additional registration step. The sync is continuous and one-way: when agents are created, updated, versioned, or deleted in API Management, those changes propagate to API Center within minutes, keeping the inventory accurate at all times. Each synchronized agent gets an associated environment and deployment record, giving developers the runtime context they need to discover and integrate the right agent with confidence. This closes the loop between runtime publishing and centralized governance, helping organizations operationalize agent ecosystems at scale without burdening platform teams with manual catalog maintenance. Why it matters By bringing agents into Azure API Center alongside APIs, plugins, skills, and MCP tools, organizations gain a single pane of glass for everything their AI applications depend on. Teams reduce duplication, improve reuse, and accelerate development — while maintaining the governance standards enterprise deployments require. Get started Azure API Center overview Set up Git-based synchronization Sync A2A agents from API Management
Data System Wide Lineage via API Request
I'm struggling with finding a solution. My goal is to identify all existing lineage relationships for any data objects within a specific data system they belong to. I've been using the Purview REST API (Datamap Dataplane) but I haven't found an endpoint returning data system side lineage/relationships. For my scenario I have a Databricks metastore and need to know the existing lineage relationships of those data objects within Purview so I can purge them out when we are doing our scheduled lineage refresh.
