Introducing Azure Managed Grafana MCP: The Managed Telemetry Gateway for AI Agents
AI agents are rapidly becoming a core part of how teams build, operate, and improve cloud systems, from coding assistants to autonomous remediation workflows. To deliver on that promise in the enterprise, agents need a secure, governed way to access real production telemetry. Azure Managed Grafana MCP lets AI agents securely query the same production telemetry you already connect to Azure Managed Grafana, like Azure Monitor metrics and logs, Application Insights, and Kusto, using your existing Azure RBAC and managed identities. How do you securely connect AI agents to real production telemetry, without standing up yet another piece of infrastructure? Today, enabling an agent to query systems like Azure Monitor, Application Insights, or Kusto often requires deploying and operating a self‑hosted MCP server, wiring up identity and networking, and maintaining additional runtime infrastructure. That friction slows adoption and expands the security surface area. Azure Managed Grafana MCP removes that entire layer. With this release, every Azure Managed Grafana instance now includes a fully managed, remote MCP server that is ready by default. What is Azure Managed Grafana MCP? Azure Managed Grafana MCP is a built‑in, managed MCP endpoint that allows AI agents to securely query enterprise telemetry and operational data through Azure Managed Grafana. Instead of deploying your own MCP server, customers can simply: Point their agent to the Azure Managed Grafana MCP endpoint Grant the agent a managed identity Start querying production data immediately No containers. No extra infrastructure. No duplicated auth systems. Azure Managed Grafana MCP is very easy to configure with your existing AMG instance Because most Azure Managed Grafana customers already connect data sources like Azure Monitor metrics, logs, Kusto, and Application Insights to Azure Managed Grafana, the MCP server can expose that telemetry to AI agents instantly, using the same RBAC and access controls teams already trust. Why we built this As we’ve talked with customers experimenting with Foundry and coding agents, a consistent theme has emerged: agents are only as useful as the data they can reason over. Requiring teams to stand up and operate a separate MCP layer introduces real cost: Additional infrastructure to deploy and maintain Custom identity and token handling Expanded attack surface Slower experimentation and adoption This Azure Managed Grafana MCP takes a different approach. Rather than asking customers to build new infrastructure for agents, we leverage infrastructure they already run and trust: Azure Managed Grafana. This shifts Grafana from being just a visualization layer to something more strategic: A secure telemetry access plane An analytical engine for agent reasoning A bridge between operational data and autonomous action Core value propositions Zero infrastructure overhead Azure Managed Grafana MCP is fully managed and enabled by default: No self‑hosted MCP servers No additional networking configuration Agents connect directly to Azure Managed Grafana and start querying data. Secure by design Security is not bolted on, it’s inherited: Uses existing Azure RBAC Supports managed identities Respects current Azure Managed Grafana access controls There’s no need to duplicate authentication or authorization logic, and the security posture remains consistent with existing observability access patterns. Immediate enterprise scenarios By exposing production telemetry through MCP, teams can unlock high‑value agent workflows immediately: Root cause analysis using Application Insights Automated operational summaries Real‑time diagnostics Cross‑resource telemetry correlation Structured data access via Kusto Chatting with an agent using Azure Managed Grafana MCP These are scenarios customers already run manually today and this MCP server makes them accessible to agents. Closing the loop: from insight to action One of the most powerful aspects of Azure Managed Grafana MCP is what happens when agents have access to both code context and live telemetry. For example: An agent queries Application Insights for production errors Identifies recurring exception patterns Locates the source code emitting those errors Generates a fix and submits a pull request This closes the loop between observability and remediation, something that’s been largely manual until now. Designing for agents, not just dashboards Humans and agents consume data very differently. Humans: Navigate dashboards sequentially Are limited by cognitive bandwidth Correlate issues manually Agents: Process large datasets in parallel Perform iterative drill‑downs without fatigue Detect statistically significant patterns quickly Azure Managed Grafana MCP is designed with this in mind. Instead of only exposing raw data, it enables agent‑optimized tools, like aggregated failure views across dozens of Application Insights instances, so agents can reason efficiently at scale. To make it easier for our customers, it is now available as a native tool within Microsoft Foundry, so you can easily connect it to your Foundry Agents. Azure Managed Grafana MCP as a native Foundry tool Looking ahead Azure Managed Grafana MCP is the foundation for a broader vision: Observability‑driven autonomous agents Secure enterprise telemetry reasoning AI systems that detect, diagnose, and act Over time, this transforms Azure Managed Grafana from dashboard software into a strategic AI integration layer for Azure. This isn’t just a visualization feature. It’s an infrastructure shift. Check out the doc for more information: Configure an Azure Managed Grafana remote MCP server | Microsoft LearnFebruary 2026 Recap: Azure Database for PostgreSQL
Hello Azure Community, We’re excited to share the February 2026 recap for Azure Database for PostgreSQL, featuring a set of updates focused on speed, simplicity, and better visibility. From Terraform support for Elastic Clusters and a refreshed VM SKU selection experience in the Azure portal to built‑in Grafana dashboards, these improvements make it easier to build, operate, and scale PostgreSQL on Azure. This recap also includes practical GIN index tuning guidance, enhancements to the PostgreSQL VS Code extension, and improved connectivity for azure_pg_admin users. Features Terraform support for Elastic Clusters - Generally Available Dashboards with Grafana - Generally Available Easier way to choose VM SKUs on portal – Generally Available What’s New in the PostgreSQL VS Code Extension Priority Connectivity to azure_pg_admin users Guide on 'gin_pending_list_limit' indexes Terraform support for Elastic Clusters Terraform now supports provisioning and managing Azure Database for PostgreSQL Elastic Clusters, enabling customers to define and operate elastic clusters using infrastructure‑as‑code workflows. With this support, it is now easier to create, scale, and manage multi‑node PostgreSQL clusters through Terraform, making it easier to automate deployments, replicate environments, and integrate elastic clusters into CI/CD pipelines. This improves operational consistency and simplifies management for horizontally scalable PostgreSQL workloads. Learn more about building and scaling with Azure Database for PostgreSQL elastic clusters. Dashboards with Grafana — Now Built-In Grafana dashboards are now natively integrated into the Azure Portal for Azure Database for PostgreSQL. This removes the need to deploy or manage a separate Grafana instance. With just a few clicks, you can visualize key metrics and logs side by side, correlate events by timestamp, and gain deep insights into performance, availability, and query behavior all in one place. Whether you're troubleshooting a spike, monitoring trends, or sharing insights with your team, this built-in experience simplifies day-to-day observability with no added cost or complexity. Try it under Azure Portal > Dashboards with Grafana in your PostgreSQL server view. For more details, see the blog post: Dashboards with Grafana — Now in Azure Portal for PostgreSQL. Easier way to choose VM SKUs on portal We’ve improved the VM SKU selection experience in the Azure portal to make it easier to find and compare the right compute options for your PostgreSQL workload. The updated experience organizes SKUs in a clearer, more scannable view, helping you quickly compare key attributes like vCores and memory without extra clicks. This streamlined approach reduces guesswork and makes selecting the right SKU faster and more intuitive. What’s New in the PostgreSQL VS Code Extension The VS Code extension for PostgreSQL helps developers and database administrators work with PostgreSQL directly from VS Code. It provides capabilities for querying, schema exploration, diagnostics, and Azure PostgreSQL management allowing users to stay within their editor while building and troubleshooting. This release focuses on improving developer productivity and diagnostics. It introduces new visualization capabilities, Copilot-powered experiences, enhanced schema navigation, and deeper Azure PostgreSQL management directly from VS Code. New Features & Enhancements Query Plan Visualization: Graphical execution plans can now be viewed directly in the editor, making it easier to diagnose slow queries without leaving VS Code. AGE Graph Rendering: Support is now available for automatically rendering graph visualizations from Cypher queries, improving the experience of working with graph data in PostgreSQL. Object Explorer Search: A new graphical search experience in Object Explorer allows users to quickly find tables, views, functions, and other objects across large schemas, addressing one of the highest-rated user feedback requests. Azure PostgreSQL Backup Management: Users can now manage Azure Database for PostgreSQL backups directly from the Server Dashboard, including listing backups and configuring retention policies. Server Logs Dashboard: A new Server Dashboard view surfaces Azure Database for PostgreSQL server logs and retention settings for faster diagnostics. Logs can be opened directly in VS Code and analyzed using the built-in GitHub Copilot integration. This release also includes several reliability improvements and bug fixes, including resolving connection pool exhaustion issues, fixing Docker container creation failures when no password is provided, and improving stability around connection profiles and schema-related operations. Priority Connectivity to azure_pg_admin Users Members of the azure_pg_admin role can now use connections from the pg_use_reserved_connections pool. This ensures that an admin always has at least one available connection, even if all standard client connections from the server connection pool are in use. By making sure admin users can log in when the client connection pool is full, this change prevents lockout situations and lets admins handle emergencies without competing for available open connection slots. Guide on 'gin_pending_list_limit' indexes Struggling with slow GIN index inserts in PostgreSQL? This post dives into the often-overlooked gin_pending_list_limit parameter and how it directly impacts insert performance. Learn how GIN’s pending list works, why the right limit matters, and practical guidance on tuning it to strike the perfect balance between write performance and index maintenance overhead. For a deeper dive into gin_pending_list_limit and tuning guidance, see the full blog here. Learning Bytes Create Azure Database for PostgreSQL elastic clusters with terraform: Elastic clusters in Azure Database for PostgreSQL let you scale PostgreSQL horizontally using a managed, multi‑node architecture. With Elastic cluster now generally available, you can provision and manage elastic clusters using infrastructure‑as‑code, making it easier to automate deployments, standardize environments, and integrate PostgreSQL into CI/CD workflows. Elastic clusters are a good fit when you need: Horizontal scale for large or fast‑growing PostgreSQL workloads Multi‑tenant applications or sharded data models Repeatable and automated deployments across environments The following example shows a basic Terraform configuration to create an Azure Database for PostgreSQL flexible server configured as an elastic cluster. resource "azurerm_postgresql_flexible_server" "elastic_cluster" { name = "pg-elastic-cluster" resource_group_name = <rg-name> location = <region> administrator_login = var.admin_username administrator_password = var.admin_password version = "17" sku_name = "GP_Standard_D4ds_v5" storage_mb = 131072 cluster { size = 3 } } Conclusion That’s a wrap for the February 2026 Azure Database for PostgreSQL recap. We’re continuing to focus on making PostgreSQL on Azure easier to build, operate, and scale whether that’s through better automation with Terraform, improved observability, or a smoother day‑to‑day developer and admin experience. Your feedback is important to us, have suggestions, ideas, or questions? We’d love to hear from you: https://aka.ms/pgfeedback.Introducing Azure Managed Grafana 12
In this release, Azure Managed Grafana makes it easier to tighten access with current-user Entra authentication, speed up Azure Monitor logs exploration, and level up Prometheus and database monitoring experiences. What’s new in Azure Managed Grafana 12 Use current-user Entra authentication for supported Azure data sources to query with the signed-in user’s permissions. Analyze Azure Monitor logs faster with a new query builder and improved visualization and Explore experiences. Explore Prometheus metrics with improved drill-down, prefix and suffix filters, group-by label support, plus OpenTelemetry and native histogram support. Use updated, pre-built database monitoring dashboards for Azure PostgreSQL, Azure SQL, and SQL Managed Instance (SQL MI). Advanced authentication: query with current user’s Entra credentials Current-user Entra authentication is now available in Azure data sources. That means Grafana admins can configure supported data sources to re-use the logged-in user’s credentials when issuing queries. In practice, the signed-in user’s permissions define what data stores they can access, helping teams apply least-privilege access to each user while keeping the option to use Managed Identities and Service Principals in other data sources where that fits best. Supported data sources include: Azure Monitor Azure Data Explorer Azure Monitor Managed Service for Prometheus Faster log analysis: Click-to-build queries and smoother Explore If you live in Azure Monitor logs, this update is for you. Improvements to log visualization in the Logs visualization panel and Grafana Explore make it easier to filter and extract meaningful insights from Azure Monitor logs. There’s also a new Azure Monitor logs query builder, so you can create and refine queries with a few clicks instead of writing Kusto Query Language (KQL) by hand. Performance is significantly faster too. Grafana Explore can now query and render up to 30K log records at a time, so you get much faster load times, faster searches, and more responsive navigation through large log volumes. Prometheus query enhancements: drill down without the query gymnastics Users new to Prometheus get a smoother path to explore metrics and analyze time series. Metrics drill-down now includes sidebar filters for prefix/suffix so you can quickly narrow metrics by naming conventions, and group-by label support to build more context-rich groupings. This is a true queryless exploration of Azure Managed Prometheus metrics when you’re troubleshooting or just identifying what’s been collected. This release also adds OpenTelemetry & native histogram support, including an OTel mode to automate label-join complexities when querying OTLP metrics. New database monitoring dashboards Azure Managed Grafana now includes new versions of pre-built dashboards for monitoring Azure Database for PostgreSQL and Azure SQL Databases (Preview). For teams building on Azure-native databases, these updated dashboards can help you get to a useful baseline faster, so you spend less time wiring panels and more time acting on what the data is telling you. Getting started To try Grafana 12, you can create a new Azure Managed Grafana instance with Grafana 12 selected, or upgrade an existing instance from the Azure portal. From there, consider enabling current-user Entra authentication for supported Azure data sources, test the new Azure Monitor logs query builder in Explore for day-to-day investigations, and take the updated database dashboards for a spin if you run Azure PostgreSQL, Azure SQL, or SQL MI. Check out the doc for more information: Upgrade Azure Managed Grafana to Grafana 12 - Azure Managed Grafana.Announcing new public preview capabilities in Azure Monitor pipeline
Azure Monitor pipeline, similar to ETL (Extract, Transform, Load) process, enhances traditional data collection methods. It streamlines data collection from various sources through a unified ingestion pipeline and utilizes a standardized configuration approach that is more efficient and scalable. As Azure Monitor pipeline is used in more complex and security‑sensitive environments — including on‑premises infrastructure, edge locations, and large Kubernetes clusters — certain patterns and challenges show up consistently. Based on what we’ve been seeing across these deployments, we’re sharing a few new capabilities now available in public preview. These updates focus on three areas that tend to matter most at scale: secure ingestion, control over where pipeline instances run, and processing data before it lands in Azure Monitor. Here’s what’s new — and why it matters. Secure ingestion with TLS and mutual TLS (mTLS) Pod placement controls for Azure Monitor pipeline Transformations and Automated Schema Standardization Secure ingestion with TLS and mutual TLS (mTLS) Why is this needed? As telemetry ingestion moves beyond Azure and closer to the edge, security expectations increase. In many environments, plain TCP ingestion is no longer sufficient. Teams often need: Encrypted ingestion paths by default Strong guarantees around who is allowed to send data A way to integrate with existing PKI and certificate management systems In regulated or security‑sensitive setups, secure authentication at the ingestion boundary is a baseline requirement — not an optional add‑on. What does this feature do? Azure Monitor pipeline now supports TLS and mutual TLS (mTLS) for TCP‑based ingestion endpoints in public preview. With this support, you can: Encrypt data in transit using TLS Enable mutual authentication with mTLS, so both the client and the pipeline endpoint validate each other Use your own certificates Enforce security requirements at ingestion time, before data is accepted This makes it easier to securely ingest data from network devices, appliances, and on‑prem workloads without relying on external proxies or custom gateways. Learn more. If the player doesn’t load, open the video in a new window: Open video Pod placement controls for Azure Monitor pipeline Why is it needed? As Azure Monitor pipeline scales in Kubernetes environments, default scheduling behavior often isn’t sufficient. In many deployments, teams need more control to: Isolate telemetry workloads in multi‑tenant clusters Run pipelines on high‑capacity nodes for resource‑intensive processing Prevent port exhaustion by limiting instances per node Enforce data residency or security zone requirements Distribute instances across availability zones for better resiliency and resource use Without explicit placement controls, pipeline instances can end up running in sub‑optimal locations, leading to performance and operational issues. What does this feature do? With the new executionPlacement configuration (public preview), Azure Monitor pipeline gives you direct control over how pipeline instances are scheduled. Using this feature, you can: Target specific nodes using labels (for example, by team, zone, or node capability) Control how instances are distributed across nodes Enforce strict isolation by allowing only one instance per node Apply placement rules per pipeline group, without impacting other workloads These rules are validated and enforced at deployment time. If the cluster can’t satisfy the placement requirements, the pipeline won’t deploy — making failures clear and predictable. This gives you better control over performance, isolation, and cluster utilization as you scale. Learn more. Transformations and Automated Schema Standardization Why is this needed? Telemetry data is often high‑volume, noisy, and inconsistent across sources. In many deployments, ingesting everything as‑is and cleaning it up later isn’t practical or cost‑effective. There’s a growing need to: Filter or reduce data before ingestion Normalize formats across different sources Route data directly into standard tables without additional processing What does this feature do? Azure Monitor pipeline data transformations, already in public preview, let you process data before it’s ingested. With transformations, you can: Filter, aggregate, or reshape incoming data Convert raw syslog or CEF messages into standardized schemas Choose sample KQL templates to perform transformations instead of manually writing KQL queries Route data directly into built‑in Azure tables Reduce ingestion volume while keeping the data that matters Check out the recent blog about the transformations preview, or you can learn more here. Getting started All of these capabilities are available today in public preview as part of Azure Monitor pipeline. If you’re already using the pipeline, you can start experimenting with secure ingestion, pod placement, and transformations right away. As always, feedback is welcome as we continue to refine these features on the path to general availability.
Public Preview: Azure Monitor pipeline transformations
Overview The Azure Monitor pipeline extends the data collection capabilities of Azure Monitor to edge and multi-cloud environments. It enables at-scale data collection (data collection over 100k EPS), and routing of telemetry data before it's sent to the cloud. The pipeline can cache data locally and sync with the cloud when connectivity is restored and route telemetry to Azure Monitor in cases of intermittent connectivity. Learn more about this here - Configure Azure Monitor pipeline - Azure Monitor | Microsoft Learn Why transformations matter Lower Costs: Filter and aggregate before ingestion to reduce ingestion volume and in turn lower ingestion costs Better Analytics: Standardized schemas mean faster queries and cleaner dashboards. Future-Proof: Built-in schema validation prevents surprises during deployment. Azure Monitor pipeline solves the challenges of high ingestion costs and complex analytics by enabling transformations before ingestion, so your data is clean, structured, and optimized before it even hits your Log Analytics Workspace. Check out a quick demo here - If the player doesn’t load, open the video in a new window: Open video Key features in public preview 1. Schema change detection One of the most exciting additions is schema validation for Syslog and CEF : Integrated into the “Check KQL Syntax” button in the Strato UI. Detects if your transformation introduces schema changes that break compatibility with standard tables. Provides actionable guidance: Option 1: Remove schema-changing transformations like aggregations. Option 2: Send data to a custom tables that support custom schemas. This ensures your pipeline remains robust and compliant with analytics requirements. For example, in the picture below, extending to new columns that don't match the schema of the syslog table throws an error during validation and asks the user to send to a custom table or remove the transformations. While in the case of the example below, filtering does not modify the schema of the data at all and so no validation error is thrown, and the user is able to send it to a standard table directly. 2. Pre-built KQL templates Apply ready-to-use templates for common transformations. Save time and minimize errors when writing queries. 3. Automatic schema standardization for syslog and CEF Automatically schematize CEF and syslog data to fit standard tables without any added transformations to convert raw data to syslog/CEF from the user. 4. Advanced filtering Drop unwanted events based on attributes like: Syslog: Facility, ProcessName, SeverityLevel. CEF: DeviceVendor, DestinationPort. Reduce noise and optimize ingestion costs. 5. Aggregation for high-volume logs Group events by key fields (e.g., DestinationIP, DeviceVendor) into 1-minute intervals. Summarize high-frequency logs for actionable insights. 6. Drop unnecessary fields Remove redundant columns to streamline data and reduce storage overhead. Supported KQL sunctions 1. Aggregation summarize (by), sum, max, min, avg, count, bin 2. Filtering where, contains, has, in, and, or, equality (==, !=), comparison (>, >=, <, <=) 3. Schematization extend, project, project-away, project-rename, project-keep, iif, case, coalesce, parse_json 4. Variables for Expressions or Functions let 5. Other Functions String: strlen, replace_string, substring, strcat, strcat_delim, extract Conversion: tostring, toint, tobool, tofloat, tolong, toreal, todouble, todatetime, totimespan Get started today Head to the Azure Portal and explore the new Azure Monitor pipeline transformations UI. Apply templates, validate your KQL, and experience the power of Azure Monitor pipeline transformations. Find more information on the public docs here - Configure Azure Monitor pipeline transformations - Azure Monitor | Microsoft Learn
Announcing public preview of query-based metric alerts in Azure Monitor
Azure Monitor metric alerts are now more powerful than ever Azure Monitor metric alerts now support all Azure metrics - including platform, Prometheus, and custom metrics - giving you complete coverage for your monitoring needs. In addition, metric alerts now offer powerful query capabilities with PromQL, enabling complex logic across multiple metrics and resources. This makes it easier to detect patterns, correlate signals, and customize alerts for modern workloads like Kubernetes clusters, VMs, and custom applications. Key Benefits Full metrics coverage: metric alerts now support alerting on any Azure metrics including platform metrics, Prometheus metrics and custom metrics. PromQL-Powered Conditions: Use PromQL to select, aggregate, and transform metrics for advanced alerting scenarios. Powerful event detection: Query-based alert rules can now detect intricate patterns across multiple timeseries based on metric change ratio, complex aggregations, or comparison between different metrics and timeseries. You can also analyze metrics across different time windows to identify change in metric behavior over time. Flexible Scoping: For query-based alert rules, choose between resource-centric alerts for granular RBAC or workspace-centric alerts for cross-resource visibility. Alerting at scale: Query-based alert rules allow monitoring metrics from multiple resources within a subscription or a resource group, using a single rule. Managed Identity Support: Securely authorize queries using Azure Managed Identity, ensuring compliance and reducing credential management overhead. Customizable Notifications: Add dynamic custom properties and custom email subjects for faster triage and context-rich alerting. Reuse community alerts: Easily import and re-use PromQL alert queries from the open-source community or from other Prometheus-based monitoring systems. Supported metrics At this time, query-based metric alerts support any metrics ingested into Azure Monitor Workspace (AMW). This currently includes: Metrics collected by Azure Monitor managed service for Prometheus, from Azure Kubernetes Services clusters (AKS) or from other sources. Virtual machine OpenTelemetry (OTel) Guest OS Metrics Other OTel custom metrics collected into Azure Monitor. You can still create threshold-based metric alerts as before on Azure platform metrics. Query-based alerts on platform metrics will be added in future releases. Comparison: Query-based metric alerts vs. Prometheus rule groups alerts Query-based metric alerts serve as an alternative to alerts defined in Prometheus rule groups. Both options remain viable and execute the same PromQL-based alerting logic. However, metric alerts are natively integrated with Azure Monitor, aligning seamlessly with other Azure alert types. They now support all your metric alerting needs within the same rule type. They also offer richer functionality and greater flexibility, making them a strong choice for teams looking for consistency across Azure monitoring solutions. See the table below for detailed comparison of the two alternatives. Stay tuned - additional enhancements to metric alerts are coming in future releases! Feature Azure Prometheus rule groups Query-based metric alerts Alert rule management Part of a rule group resource Independent Azure resource Supported metrics Metrics in AMW (Managed Prometheus) Metrics in AMW (Managed Prometheus, OTel metrics) Condition logic PromQL-based query PromQL-based query Aggregation & transformation Full PromQL support Full PromQL support Scope Workspace-wide Resource-centric or workspace-wide Alerting at scale Not supported Subscription level, Resource-group level Cross-resource conditions Supported Supported RBAC granularity Workspace level Resource or workspace level Managed identity support Not supported Supported Notification customization Supported - Prometheus labels and annotations Advanced - dynamic custom properties, custom email subject Getting Started If you have an Azure Monitor workspace containing Prometheus or OpenTelemetry metrics, you can create query-based metric alert rules today. Rules can be created and managed using the Azure Portal, ARM templates, or Azure REST API. For details, visit Azure Monitor documentation.
