Microsoft Teams Blog articles Note

Microsoft Teams Blog articles

Microsoft Teams Blog on TechNet is a dedicated platform for Microsoft Teams covering various topics including upcoming features, product improvements, and best practices to enhance user experience. It contains articles by Microsoft product team members, MVPs, and other experts in the field. The blog posts address different aspects of Microsoft Teams such as configuration, deployment, troubleshoot, user feedback, and shared knowledge.

Thread Of Notes

An AI assistant connected via Azure MCP Server can now help engineers diagnose Azure application slowness. This connection allows AI to query Azure Monitor, inspect configurations, and compare resources, streamlining investigations. However, this capability introduces significant security risks if not properly managed. The primary concern is the potential for the AI to access sensitive Azure data or perform unauthorized production changes. To mitigate these risks, a layered security approach is essential, starting with strict identity and access management.Each AI interaction must be bounded by specific permissions, utilizing least-privilege principles for Entra identities and restricting access to necessary resources. Telemetry from applications must be treated as untrusted input, preventing it from being interpreted as instructions, and preventing prompt injection. Read-only access, while reducing change risk, still requires careful data handling to avoid exposing sensitive information like tokens or customer data.Targeting the correct Azure resource is critical, with validation checks and approval workflows for consequential actions. Data-plane permissions, especially for services like Key Vault, must be separated from management-plane access to prevent credential exposure. The Azure MCP Server itself needs to be secured, with credentials managed carefully and access restricted. Automation’s amplification of mistakes necessitates implementing limits on operations and robust error handling. Azure Policy and governance provide essential guardrails, and comprehensive logging is crucial for observability and auditing of all AI-driven activities.
Microsoft Marketplace has launched 229 new solutions for cloud, AI, and business needs. Accenture NewsPage digitizes Route-To-Market value chains for distributors. Assurance Map visualizes organizational vulnerabilities and integrates with Microsoft Teams. Ataytis Motion adds animated charts to Power BI reports for trend visualization. Blekline Mark and Redact offer governance for AI coding agents on Kubernetes. Charm Security's AI workforce combats human-centric fraud. CSCloudSolutions FinOps provides actionable insights from Azure billing. Digital Gov DMS is a document management solution for the public sector. Dimension Management Transact simplifies dimension management in Dynamics 365 Business Central. Dropbox Sign integrates document signing into Dynamics 365 Business Central. Elsagility streamlines workflow management for client accounts. Humanly automates candidate engagement for faster hiring. Kepion EPM is a financial planning platform native to Microsoft. Keytos Shield offers cloud-based passwordless authentication. Kyvoi provides continuous monitoring and security assessment for Microsoft 365. LFM2-24B-A2B-InsilicoMMAI-Chem is a drug discovery large language model. QNSI is a quantum-native security platform for cryptographic asset management. Routebase is an API lifecycle platform for streamlined development. Shift Handover Log improves team handovers with task tracking. Simmie is an AI sales readiness platform for realistic simulations. SqlCertForge securely manages SQL Server certificates. Symitri Prism uses first-party data for e-commerce campaign decisions. Tenant Strike is a security scanner for Microsoft 365 and Azure. LG CNS offers private GPU services for AI infrastructure on Azure. ThePoint provides SharePoint web parts for quick intranet creation. Titan SFTP Server on AKS offers secure file transfers. V-Track centralizes collateral lifecycle management for banks. VeriForm Health generates synthetic healthcare datasets for AI. VR Payment integrates QR-code payments into Dynamics 365 Business Central. Your DMARC Exposed simplifies DMARC report analysis. Zentrix Advanced Gantt provides advanced project scheduling in Power BI.
Endpoint and AI strategies are merging as AI workloads become more distributed across capable devices. IT leaders must now consider how device decisions impact future AI platforms. Enterprise AI has focused on platforms, models, and cloud services, but AI PCs introduce endpoints as crucial execution environments. Different AI workloads necessitate varied execution environments, with sensitive or latency-critical tasks benefiting from local processing. Cloud models remain ideal for large-scale or complex reasoning tasks. Hybrid intelligence integrates local and cloud resources, making the device an active part of the AI platform. Agents, operating over time and accessing data, further complicate architectural decisions. This intersection impacts application design, security, and cost, requiring collaboration between endpoint, security, and application teams. Organizations can standardize devices while offering specialized configurations for demanding AI workloads. Existing cloud-based AI solutions can be extended by local inference, distributing processing between device and cloud. Failure and fallback strategies are crucial for both endpoint and cloud-based workloads, impacting security, privacy, and reliability. Agentic applications require controls for file access, tool usage, and activity across environments. Technologies like Microsoft Execution Containers and Microsoft Agent 365 aim to manage agent activity as part of an enterprise. Workload placement depends on factors like response time, privacy, connectivity, and available resources. Hybrid runtimes can dynamically route tasks to appropriate local or cloud resources based on real-time conditions. Software development showcases this by leveraging cloud models for complex problems and local models for targeted tasks. Local execution requires compatible models, runtimes, application paths, and sufficient system capacity. Windows ML provides a foundation for running supported models across various hardware resources. Incorporating local inference means hardware becomes an integral part of the architecture, influencing performance and cost. Device planning should consider employee roles and potential dedicated local infrastructure. Field inspection workflows and copilot illustrate hybrid intelligence by combining local and cloud capabilities. Local AI workloads must be evaluated within the context of overall device activity. Device manufacturers are designing hardware to support evolving AI landscapes, considering compute, RAM, and power. Testing applications on representative hardware is crucial for understanding real-world performance and making informed placement decisions. The economics of local inference involve trade-offs between local compute costs and cloud inference expenses.
Azure SQL Database offers seamless compute scaling without infrastructure management. Scaling operations involve more than just adjusting vCores; they include provisioning new compute, preparing data, switching connections, and cleaning up the old environment. The time taken for scaling can significantly differ even for seemingly identical changes between two databases.Database size is a key factor, as larger databases require more data preparation on the target compute. The active workload on a database can also influence scaling duration, as the database remains online during target environment preparation. Long-running transactions present at the time of switchover can delay the connection transition and prolong the operation. Resource availability within Azure SQL can also cause operations to enter a pending state, adding to the overall time.Monitoring scaling operations can be done using Azure PowerShell's Get-AzSqlDatabaseActivity and Get-AzSqlDatabase cmdlets. The sys.dm_operation_status DMV provides an SQL-side view of operation states like PENDING and IN_PROGRESS. The PercentComplete column in sys.dm_operation_status typically indicates operation status rather than granular progress for scaling.During scaling, the database remains available, with connections continuing to the original compute until switchover. The switchover itself causes a brief interruption, requiring applications to implement retry logic. A slow scaling operation doesn't necessarily indicate failure; it might be legitimately working through complex preparation stages. Troubleshooting involves verifying the exact changes, comparing database sizes, workload activity, and checking for long-running transactions.
A recent session demonstrated how to investigate SQL performance issues by combining Query Store and SSMS Copilot. The demo involved a PHP program that grew a table, causing performance changes and different query execution plans. A key observation was a query's execution time increasing dramatically under the same Plan ID, indicating degradation within a plan. The session showed how Copilot could guide investigations, evolving from Ask mode to Agent mode, and how these processes could be captured as reusable skills. Query Store provided historical performance data, while Copilot aided in analyzing this data and deciding on next steps. The practical demonstration used a PHP program to simulate various performance problems by adding records to a table in stages. As data volume increased, the execution plans and performance of the dbo.GiveNotes query evolved. A detailed comparison revealed a significant increase in CPU, duration, and logical reads for one Plan ID, even before a new plan was introduced. The investigation focused on building a timeline of changes and understanding the aggregated impact of workload contributors. Weighted averages were crucial for accurately assessing performance across different intervals and execution counts. The increase in logical reads pointed towards data processing and access path issues. The session explored three ways to interact with Copilot: Ask mode for guided questions, Agent mode for autonomous diagnostics, and reusable Agent Skills for standardized investigations. The SQL Performance Investigator pack was developed, instructing the agent to identify contributors, analyze plan history, check for degradation within plans, and investigate selectivity, statistics, and indexes. A proposed index was evaluated, but its actual improvement required further validation against a comparable workload. The core lesson was that understanding performance changes requires examining both differences between plans and variations within a single plan's history.
When an Azure SQL Database experiences performance issues, simply checking CPU utilization is insufficient. Queries may spend most of their time waiting rather than executing, even with healthy resource metrics. The crucial question is where the elapsed time is being spent, not just how much CPU is being used. Resource utilization should be the initial checkpoint, and a query on sys.dm_db_resource_stats can identify if CPU, Data IO, Log IO, memory, or worker limits are being approached. If resources are not saturated, then analysis of wait statistics is necessary.Wait patterns, like PAGELATCH_* for in-memory contention or LCK_M_* for blocking, indicate areas to investigate further. PAGEIOLATCH_* waits are linked to physical I/O, while PAGELATCH_* waits concern synchronization on pages already in memory and can be caused by high concurrency on a single hot page. The frequency of query executions also significantly impacts performance, not just individual query cost. Query Store is vital for tracking query execution history, allowing comparison of durations, CPU usage, and wait behaviors over time.If a query has a long elapsed time but low CPU usage, investigate waits such as ASYNC_NETWORK_IO, which often indicates the client application is not consuming results quickly enough. Measuring time to the first row versus time to the last row can help differentiate database response time from result consumption time. Persistent blocking, where sessions wait for another transaction, can also cause perceived slowness even with low CPU. Scaling a database can mask the root cause by improving general resource availability rather than addressing the underlying performance bottleneck. A practical workflow involves defining incident windows, checking resources, comparing CPU to elapsed time, identifying dominant waits, checking blocking, using Query Store, inspecting execution plans, and investigating the application. Ultimately, effectively troubleshooting Azure SQL performance requires understanding where time is spent, rather than just how busy the database appears.
This blog post discusses high-availability (HA) architecture for mission-critical Oracle databases on Microsoft Azure, specifically using FlashGrid Cluster to enable Oracle RAC across availability zones. Organizations face choices between Oracle AI Database@Azure, self-managed single instances, or Oracle RAC. Oracle Data Guard helps protect against availability zone failures by maintaining a standby database.Oracle RAC offers active-active HA with zero downtime during rolling maintenance and near-zero recovery time objectives (RTO) during unexpected failures, outperforming Data Guard for mission-critical HA. Azure recommends multi-VM deployments across availability zones for 99.99% uptime SLA. Two multi-AZ options for Oracle RAC are proposed: two RAC clusters with Data Guard replication or one RAC cluster spanning two or three availability zones.Option A, two RAC clusters with Data Guard, duplicates resources, increasing cost and operational complexity. It also introduces Data Guard failover time, impacting the uptime SLA. Option B, a single multi-AZ RAC cluster, offers the same HA capabilities as a local RAC cluster, plus protection against availability zone failures. FlashGrid Cluster simplifies this complex deployment with automated Azure Resource Manager templates and pre-tested components.Deployment takes only 30-90 minutes, making it faster and simpler than typical on-premises Oracle RAC deployments. Network latency between Azure availability zones is typically less than 1ms, not critically impacting most workloads. For latency-sensitive Cache Fusion operations, a preferred database instance can minimize cross-node traffic.Neither option provides a complete disaster recovery solution; a multi-region DR strategy is needed for regional outages, typically using asynchronous Data Guard or Oracle GoldenGate. FlashGrid Cluster automates the infrastructure for multi-AZ Oracle RAC on Azure, offering rapid recovery, rolling maintenance support, and protection against availability zone failures without duplicating clusters or relying on Data Guard FSFO. FlashGrid Cluster is available on the Microsoft Marketplace, with production deployments since 2017.
This document explores Jev-style typed-decision interfaces for accelerating application workflows, focusing on semantic understanding leading to structured actions. It highlights how models can make fast, bounded decisions in applications like order processing, contrasting with traditional rules for explicit constraints and large models for open-ended problems. The core concept is using Small Language Models (SLMs) for frequent, localized decisions, keeping the decision close to the action. This approach is beneficial for vertical applications and embodied systems, where low-latency, task-level intent decisions are crucial.The architecture emphasizes a division of labor: large models for open-ended tasks, small models for bounded semantic decisions, and deterministic code for constraints. The document details a process of fine-tuning a general decision component using Qwen3.5-0.8B, employing LoRA fine-tuning and exporting to ONNX. This workflow serves as a concrete path from data to a deployable decision capability, focusing on learning bounded choices rather than long answers.Preprocessing expands each question into training examples, ensuring decision types like choice, noul, and score align with specific output formats. The Olive workflow, using Microsoft Olive, standardizes fine-tuning settings and ensures a reproducible process. A key distinction is the use of standard full-sequence language-model SFT, not the Hmm project's candidate-letter mixture. The export process uses ModelBuilder to package the base model, adapter, tokenizer, and configuration into a deployable ONNX unit.Navigating version boundaries and compatibility issues during the export process is highlighted as a critical learning point, emphasizing the need for robust version management. Testing involves two stages: ensuring the model runs and verifying that its decisions are usable by the application. This includes extracting probabilities from option-letter logits to interpret the model's output as distributions for choice, noul, and score types, providing a measure of concentration rather than calibrated correctness.