Model Mondays S2E12: Models & Observability
1. Weekly Highlights This week’s top news in the Azure AI ecosystem included: GPT Real Time (GA): Azure AI Foundry now offers GPT Real Time (GA)—lifelike voices, improved instruction following, audio fidelity, and function calling, with support for image context and lower pricing. Read the announcement and check out the model card for more details. Azure AI Translator API (Public Preview): Choose between fast Neural Machine Translation (NMT) or nuanced LLM-powered translations, with real-time flexibility for multilingual workflows. Read the announcement then check out the Azure AI Translator documentation for more details. Azure AI Foundry Agents Learning Plan: Build agents with autonomous goal pursuit, memory, collaboration, and deep fine-tuning (SFT, RFT, DPO) - on Azure AI Foundry. Read the announcement what Agentic AI involves - then follow this comprehensive learning plan with step-by-step guidance. CalcLM Agent Grid (Azure AI Foundry Labs): Project CalcLM: Agent Grid is a prototype and open-source experiment that illustrates how agents might live in a grid-like surface (like Excel). It's formula-first and lightweight - defining agentic workflows like calculations. Try the prototype and visit Foundry Labs to learn more. Agent Factory Blog: Observability in Agentic AI: Agentic AI tools and workflows are gaining rapid adoption in the enterprise. But delivering safe, reliable and performant agents requires foundation support for Observability. Read the 6-part Agent Factory series and check out the Top 5 agent observability best practices for reliable AI blog post for more details. 2. Spotlight On: Observability in Azure AI Foundry This week’s spotlight featured a deep dive and demo by Han Che (Senior PM, Core AI/ Microsoft ), showing observability end-to-end for agent workflows. Why Observability? Ensures AI quality, performance, and safety throughout the development lifecycle. Enables monitoring, root cause analysis, optimization, and governance for agents and models. Key Features & Demos: Development Lifecycle: Leaderboard: Pick the best model for your agent with real-time evaluation. Playground: Chat and prototype agents, view instant quality and safety metrics. Evaluators: Assess quality, risk, safety, intent resolution, tool accuracy, code vulnerability, and custom metrics. Governance: Integrate with partners like Cradle AI and SideDot for policy mapping and evidence archiving. Red Teaming Agent: Automatically test for vulnerabilities and unsafe behavior. CI/CD Integration: Automate evaluation in GitHub Actions and Azure DevOps pipelines. Azure DevOps GitHub Actions Monitoring Dashboard: Resource usage, application analytics, input/output tokens, request latency, cost breakdown, and evaluation scores. Azure Cost Management SDKs & Local Evaluation: Run evaluations locally or in the cloud with the Azure AI Evaluation SDK. Demo Highlights: Chat with a travel planning agent, view run metrics and tool usage. Drill into run details, debugging, and real-time safety/quality scores. Configure and run large-scale agent evaluations in CI/CD pipelines. Compare agents, review statistical analysis, and monitor in production dashboards 3. Customer Story: Saifr Saifr is a RegTech company that uses artificial intelligence to streamline compliance for marketing, communications, and creative teams in regulated industries. Incubated at Fidelity Labs (Fidelity Investments’ innovation arm), Saifr helps enterprises create, review, and approve content that meets regulatory standards—faster and with less manual effort. What Saifr Offers AI-Powered Compliance: Saifr’s platform leverages proprietary AI models trained on decades of regulatory expertise to automatically detect potential compliance risks in text, images, audio, and video. Automated Guardrails: The solution flags risky or non-compliant language, suggests compliant alternatives, and provides explanations—all in real time. Workflow Integration: Saifr seamlessly integrates with enterprise content creation and approval workflows, including cloud platforms and agentic AI systems like Azure AI Foundry. Multimodal Support: Goes beyond text to check images, videos, and audio for compliance risks, supporting modern marketing and communications teams. 4. Key Takeaways Observability is Essential: Azure AI Foundry offers complete monitoring, evaluation, tracing, and governance for agentic AI—making production safe, reliable, and compliant. Built-In Evaluation and Red Teaming: Use leaderboards, evaluators, and red teaming agents to assess and continuously improve model safety and quality. CI/CD and Dashboard Integration: Automate evaluations in GitHub Actions or Azure DevOps, then monitor and optimize agents in production with detailed dashboards. Compliance Made Easy: Safer’s agents and models help financial services and regulated industries proactively meet compliance standards for content and communications. Sharda's Tips: How I Wrote This Blog I focus on organizing highlights, summarizing customer stories, and linking to official Microsoft docs and real working resources. For this recap, I explored the Azure AI Foundry Observability docs, tested CI/CD pipeline integration, and watched the customer demo to share best practices for regulated industries. Here’s my Copilot prompt for this episode: "Generate a technical blog post for Model Mondays S2E12 based on the transcript and episode details. Focus on observability, agent dashboards, CI/CD, compliance, and customer stories. Add correct, working Microsoft links!" Coming Up Next Week Next week: Open Source Models! Join us for the final episode with Hugging Face VP of Product, live demos, and open model workflows. Register For The Livestream – Sep 15, 2025 About Model Mondays Model Mondays is your weekly Azure AI learning series: 5-Minute Highlights: Latest AI news and product updates 15-Minute Spotlight: Demos and deep dives with product teams 30-Minute AMA Fridays: Ask anything in Discord or the forum Start building: Watch Past Replays Register For AMA Recap Past AMAs Join The Community Don’t build alone! The Azure AI Developer Community is here for real-time chats, events, and support: Join the Discord Explore the Forum About Me I'm Sharda, a Gold Microsoft Learn Student Ambassador focused on cloud and AI. Find me on GitHub, Dev.to, Tech Community, and LinkedIn. In this blog series, I share takeaways from each week’s Model Mondays livestream.
Sentinel Data Connector for Azure Virtual Desktop
Hello, I have a customer planning to deploy Azure Virtual Desktop (AVD). They are currently using Microsoft Sentinel for their SecOps. However, there is no AVD Data Connector available. The customer is not interested in building a custom data connector. Does anyone know if there are plans to add a data connector for AVD in the near future? Thanks.
Workbook with multiple visualizations using lowest number of queries
Coming from Splunk world and didn't found answer to this in the workbook documentation. Is it possible to chains searches, like in Splunk, explained here: https://docs.splunk.com/Documentation/Splunk/9.3.1/DashStudio/dsChain Trying to explain in KQL terms: suppose there are 3 very similar queries, like same base search | condition 1 same base search | condition 2 same base search | condition 3 feeding 3 vizualizations. Goal is to execute the "same base search" part only once in the workbook. Defining a new function for "same base search" still means 3 executions, I guess. Your response is appreciated. Thank you.
Cross-workspace incident management
Hello Techcommunity, We are looking for a solution to manage incidents in several Sentinel workspaces within the same tenant. 1. We reviewed Azure Lighthouse and it seems to be working only for cross-tenant management 2. We saw the option to mark the workspaces we want to monitor and click on "View incidents" 3. We also considered building the dashboard in a Workbook Could you please say if there is any other option to have a unified dashboard for managing incidents from several Sentinels within the same tenant?
Sentinel Log Sources or asset list Information
In Sentinel as like any other SIEM, how do we get the complete list of log sources which are integrated along with some required fields like Device Vendor, Device Product, Host name/Computer, IP address. Is there any workbook or KQL which provides this information.
