Retina 1.0 Is Now Available
We are excited to announce the first major release of Retina - a significant milestone for the project. This version brings along many new features, enhancements and bug fixes. The Retina maintainer team would like to thank all contributors, community members, and early adopters who helped make this 1.0 release possible. What is Retina? Retina is an open-source, Kubernetes network observability platform. It enables you to continuously observe and measure network health, and investigate network issues on-demand with integrated Kubernetes-native workflows. Why Retina? Kubernetes networking failures are rarely isolated or easy to reproduce. Pods are ephemeral, services span multiple nodes, and network traffic crosses multiple layers (CNI, kube-proxy, node networking, policies), making crucial evidence difficult to capture. Manually connecting to nodes and stitching together logs or packet captures simply does not scale as clusters grow in size and complexity. A modern approach to observability must automate and centralize data collection while exposing rich, actionable insights. Retina represents a major step forward in solving the complexities of Kubernetes observability by leveraging the power of eBPF. Its cloud-agnostic design, deep integration with Hubble, and support for both real-time metrics and on-demand packet captures make it an invaluable tool for DevOps, SecOps, and compliance teams across diverse environments. What Does It Do? Retina can collect two types of telemetry: metrics and packet captures. The Retina shell enables ad-hoc troubleshooting via pre-installed networking tools. Metrics Metrics provide continuous observability. They can be exported to multiple storage options such as Prometheus or Azure Monitor, and visualized in a variety of ways, including Grafana or Azure Log Analytics. Retina supports two control planes: Hubble and Standard. Both are supported regardless of the underlying CNI. The choice of control plane affects the metrics which are collected. Hubble metrics Standard metrics You can customize which metrics are collected by enabling/disabling their corresponding plugins. Some examples of metrics may include: Incoming/outcoming traffic Dropped packets TCP/UDP DNS API Server latency Node/interface statistics Packet Captures Captures provide on-demand observability. They allow users to perform distributed packet captures across the cluster, based on specified Nodes/Pods and other supported filters. They can be triggered via the CLI or through the capture CRD, and may be output to persistent storage options such as the host filesystem, a PVC, or a storage blob. The result of the capture contains more than just a .pcap file. Retina also captures a number of networking metadata such as iptables rules, socket statistics, kernel network information from /proc/net, and more. Shell The Retina shell enables deep ad-hoc troubleshooting by providing a suite of networking tools. The CLI command starts an interactive shell on a Kubernetes node that runs a container image which includes standard tools such as ping or curl, as well as specialized tools like bpftool, pwru, Inspektor Gadget and more. The Retina shell is currently only available on Linux. Note that some tools require particular capabilities to execute. These can be passed as parameters through the CLI. Use Cases Debugging Pod Connectivity Issues: When services can’t communicate, Retina enables rapid, automated distributed packet capture and drop metrics, drastically reducing troubleshooting time. The Retina shell also brings specialized tools for deep manual investigations. Continuous Monitoring of Network Health: Operators can set up alerts and dashboards for DNS failures, API server latency, or packet drops, gaining ongoing visibility into cluster networking. Security Auditing and Compliance: Flow logs (in Hubble mode) and metrics support security investigations and compliance reporting, enabling quick identification of unexpected connections or data transfers. Multi-Cluster / Multi-Cloud Visibility: Retina standardizes network observability across clouds, supporting unified dashboards and processes for SRE teams. Where Does It Run? Retina is designed for broad compatibility across Kubernetes distributions, cloud providers, and operating systems. There are no Azure-specific dependencies - Retina runs anywhere Kubernetes does. Operating Systems: Both Linux and Windows nodes are supported. Kubernetes Distributions: Retina is distribution-agnostic, deployable on managed services (AKS, EKS, GKE) or self-managed clusters. CNI / Network Stack: Retina works with any CNI, focusing on kernel-level events rather than CNI-specific logs. Cloud Integration: Retina exports metrics to Azure Monitor and Log Analytics, with pre-built Grafana dashboards for AKS. Integration with AWS CloudWatch or GCP Stackdriver is possible via Prometheus. Observability Stacks: Retina integrates with Prometheus & Grafana, Cilium Hubble (for flow logs and UI), and can be extended to other exporters. Design Overview Retina’s architecture consists of two layers: a data collection layer in the kernel-space, and processing layer that converts low-level signals into Kubernetes-aware telemetry in the user-space. When Retina is installed, each node in the cluster runs a Retina agent which collects raw network telemetry from the host kernel - backed by eBPF on Linux, and HNS/VFP on Windows. The agent processes the raw network data and enriches it with Kubernetes metadata, which is then exported for consumption by monitoring tools such as Prometheus, Grafana, or Hubble UI. Modularity and extensibility are central to the design philosophy. Retina's plugin model lets you enable only the telemetry you need, and add new sources by implementing a common plugin interface. Built-in plugins include Drop Reason, DNS, Packet Forward, and more. Check out our architecture docs for a deeper dive into Retina's design. Get Started Thanks to Helm charts deploying Retina is streamlined across all environments, and can be done with one configurable command. For complete documentation, visit our installation docs. To install Retina with the Standard control plane and Basic metrics mode: VERSION=$( curl -sL https://api.github.com/repos/microsoft/retina/releases/latest | jq -r .name) helm upgrade --install retina oci://ghcr.io/microsoft/retina/charts/retina \ --version $VERSION \ --namespace kube-system \ --set image.tag=$VERSION \ --set operator.tag=$VERSION \ --set logLevel=info \ --set operator.enabled=true \ --set enabledPlugin_linux="\[dropreason\,packetforward\,linuxutil\,dns\]" Once Retina is running in your cluster, you can then configure Prometheus and Grafana to scrape and visualize your metrics. Install the Retina CLI with Krew: kubectl krew install retina Get Involved Retina is open-source under the MIT License and welcomes community contributions. Since its announcement in early 2024, the project has gained significant traction, with contributors from multiple organizations helping to expand its capabilities. The project is hosted on GitHub · microsoft/retina and documentation is available at retina.sh. If you would like to contribute to Retina you can follow our contributor guide. What's Next? Retina 1.1 of course! We are also discussing the future roadmap, and exploring the possibility of moving the project to community ownership. Stay tuned! In the meantime, we welcome you to raise an issue if you find any bugs, or start a discussion if you have any questions or suggestions. You can also reach out to the Retina team via email, we would love to hear from you! References Retina Deep Dive into Retina Open-Source Kubernetes Network Observability Troubleshooting Network Issues with Retina Retina: Bridging Kubernetes Observability and eBPF Across the Clouds
Project Pavilion Presence at KubeCon NA 2025
KubeCon + CloudNativeCon NA took place in Atlanta, Georgia, from 10-13 November, and continued to highlight the ongoing growth of the open source, cloud-native community. Microsoft participated throughout the event and supported several open source projects in the Project Pavilion. Microsoft’s involvement reflected our commitment to upstream collaboration, open governance, and enabling developers to build secure, scalable and portable applications across the ecosystem. The Project Pavilion serves as a dedicated, vendor-neutral space on the KubeCon show floor reserved for CNCF projects. Unlike the corporate booths, it focuses entirely on open source collaboration. It brings maintainers and contributors together with end users for hands-on demos, technical discussions, and roadmap insights. This space helps attendees discover emerging technologies and understand how different projects fit into the cloud-native ecosystem. It plays a critical role for idea exchanges, resolving challenges and strengthening collaboration across CNCF approved technologies. Why Our Presence Matters KubeCon NA remains one of the most influential gatherings for developers and organizations shaping the future of cloud-native computing. For Microsoft, participating in the Project Pavilion helps advance our goals of: Open governance and community-driven innovation Scaling vital cloud-native technologies Secure and sustainable operations Learning from practitioners and adopters Enabling developers across clouds and platforms Many of Microsoft’s products and cloud services are built on or aligned with CNCF and open-source technologies. Being active within these communities ensures that we are contributing back to the ecosystem we depend on and designing by collaborating with the community, not just for it. Microsoft-Supported Pavilion Projects containerd Representative: Wei Fu The containerd team engaged with project maintainers and ecosystem partners to explore solutions for improving AI model workflows. A key focus was the challenge of handling large OCI artifacts (often 500+ GiB) used in AI training workloads. Current image-pulling flows require containerd to fetch and fully unpack blobs, which significantly delays pod startup for large models. Collaborators from Docker, NTT, and ModelPack discussed a non-unpacking workflow that would allow training workloads to consume model data directly. The team plans to prototype this behavior as an experimental feature in containerd. Additional discussions included updates related to nerdbox and next steps for the erofs snapshotter. Copacetic Representative: Joshua Duffney The Copa booth attracted roughly 75 attendees, with strong representation from federal agencies and financial institutions, a sign of growing adoption in regulated industries. A lightning talk delivered at the conference significantly boosted traffic and engagement. Key feedback and insights included: High interest in customizable package update sources Demand for application-level patching beyond OS-level updates Need for clearer CI/CD integration patterns Expectations around in-cluster image patching Questions about runtime support, including Podman The conversations revealed several documentation gaps and feature opportunities that will inform Copa’s roadmap and future enablement efforts. Drasi Representative: Nandita Valsan KubeCon NA 2025 marked Drasi’s first in-person presence since its launch in October 2024 and its entry into the CNCF Sandbox in early 2025. With multiple kiosk slots, the team interacted with ~70 visitors across shifts. Engagement highlights included: New community members joining the Drasi Discord and starring GitHub repositories Meaningful discussions with observability and incident management vendors interested in change-driven architectures Positive reception to Aman Singh’s conference talk, which led attendees back to the booth for deeper technical conversations Post-event follow-ups are underway with several sponsors and partners to explore collaboration opportunities. Flatcar Container Linux Representatives: Sudhanva Huruli and Vamsi Kavuru The Flatcar project had some fantastic conversations at the pavilion. Attendees were eager to learn about bare metal provisioning, GPU support for AI workloads, and how Flatcar’s fully automated build and test process keeps things simple and developer friendly. Questions around Talos vs. Flatcar and CoreOS sparked lively discussions, with the team emphasizing Flatcar’s usability and independence from an OS-level API. Interest came from government agencies and financial institutions, and the preview of Flatcar on AKS opened the door to deeper conversations about real-world adoption. The Project Pavilion proved to be the perfect venue for authentic, technical exchanges. Flux Representatives: Dipti Pai The Flux booth was active throughout all three days of the Project Pavilion, where Microsoft joined other maintainers to highlight new capabilities in Flux 2.7, including improved multi-tenancy, enhanced observability, and streamlined cloud-native integrations. Visitors shared real-world GitOps experiences, both successes and challenges, which provided valuable insights for the project’s ongoing development. Microsoft’s involvement reinforced strong collaboration within the Flux community and continued commitment to advancing GitOps practices. Headlamp Representatives: Joaquim Rocha, Will Case, and Oleksandr Dubenko Headlamp had a booth for all three days of the conference, engaging with both longstanding users and first-time attendees. The increased visibility from becoming a Kubernetes sub-project was evident, with many attendees sharing their usage patterns across large tech organizations and smaller industrial teams. The booth enabled maintainers to: Gather insights into how teams use Headlamp in different environments Introduce Headlamp to new users discovering it via talks or hallway conversations Build stronger connections with the community and understand evolving needs Inspektor Gadget Representatives: Jose Blanquicet and Mauricio Vásquez Bernal Hosting a half-day kiosk session, Inspektor Gadget welcomed approximately 25 visitors. Attendees included newcomers interested in learning the basics and existing users looking for updates. The team showcased new capabilities, including the tcpdump gadget and Prometheus metrics export, and invited visitors to the upcoming contribfest to encourage participation. Istio Representatives: Keith Mattix, Jackie Maertens, Steven Jin Xuan, Niranjan Shankar, and Mike Morris The Istio booth continued to attract a mix of experienced adopters and newcomers seeking guidance. Technical discussions focused on: Enhancements to multicluster support in ambient mode Migration paths from sidecars to ambient Improvements in Gateway API availability and usage Performance and operational benefits for large-scale deployments Users, including several Azure customers, expressed appreciation for Microsoft’s sustained investment in Istio as part of their service mesh infrastructure. Notary Project Representative: Feynman Zhou and Toddy Mladenov The Notary Project booth saw significant interest from practitioners concerned with software supply chain security. Attendees discussed signing, verification workflows, and integrations with Azure services and Kubernetes clusters. The conversations will influence upcoming improvements across Notary Project and Ratify, reinforcing Microsoft’s commitment to secure artifacts and verifiable software distribution. Open Policy Agent (OPA) - Gatekeeper Representative: Jaydip Gabani The OPA/Gatekeeper booth enabled maintainers to connect with both new and existing users to explore use cases around policy enforcement, Rego/CEL authoring, and managing large policy sets. Many conversations surfaced opportunities around simplifying best practices and reducing management complexity. The team also promoted participation in an ongoing Gatekeeper/OPA survey to guide future improvements. ORAS Representative: Feynman Zhou and Toddy Mladenov ORAS engaged developers interested in OCI artifacts beyond container images which includes AI/ML models, metadata, backups, and multi-cloud artifact workflows. Attendees appreciated ORAS’s ecosystem integrations and found the booth examples useful for understanding how artifacts are tagged, packaged, and distributed. Many users shared how they leverage ORAS with Azure Container Registry and other OCI-compatible registries. Radius Representative: Zach Casper The Radius booth attracted the attention of platform engineers looking for ways to simplify their developer's experience while being able to enforce enterprise-grade infrastructure and security best practices. Attendees saw demos on deploying a database to Kubernetes and using managed databases from AWS and Azure without modifying the application deployment logic. They also saw a preview of Radius integration with GitHub Copilot enabling AI coding agents to autonomously deploy and test applications in the cloud. Conclusion KubeCon + CloudNativeCon North America 2025 reinforced the essential role of open source communities in driving innovation across cloud native technologies. Through the Project Pavilion, Microsoft teams were able to exchange knowledge with other maintainers, gather user feedback, and support projects that form foundational components of modern cloud infrastructure. Microsoft remains committed to building alongside the community and strengthening the ecosystem that powers so much of today’s cloud-native development. For anyone interested in exploring or contributing to these open source efforts, please reach out directly to each project’s community to get involved, or contact Lexi Nadolski at lexinadolski@microsoft.com for more information.Beyond the Chat Window: How Change-Driven Architecture Enables Ambient AI Agents
AI agents are everywhere now. Powering chat interfaces, answering questions, helping with code. We've gotten remarkably good at this conversational paradigm. But while the world has been focused on chat experiences, something new is quietly emerging: ambient agents. These aren't replacements for chat, they're an entirely new category of AI system that operates in the background, sensing, processing, and responding to the world in real time. And here's the thing, this is a new frontier. The infrastructure we need to build these systems barely exists yet. Or at least, it didn't until now. Two Worlds: Conversational and Ambient Let me paint you a picture of the conversational AI paradigm we know well. You open a chat window. You type a question. You wait. The AI responds. Rinse and repeat. It's the digital equivalent of having a brilliant assistant sitting at a desk, ready to help when you tap them on the shoulder. Now imagine a completely different kind of assistant. One that watches for important changes, anticipates needs, and springs into action without being asked. That's the promise of ambient agents. AI systems that, as LangChain puts it: "listen to an event stream and act on it accordingly, potentially acting on multiple events at a time." This isn't an evolution of chat; it's a fundamentally different interaction paradigm. Both have their place. Chat is great for collaboration and back-and-forth reasoning. Ambient agents excel at continuous monitoring and autonomous response. Instead of human-initiated conversations, ambient agents operate through detecting changes in upstream systems and maintaining context across time without constant prompting. The use cases are compelling and distinct from chat. Imagine a project management assistant that operates in two modes: you can chat with it to ask, "summarize project status", but it also runs in the background, constantly monitoring new tickets that are created, or deployment pipelines that fail, automatically reassigning tasks. Or consider a DevOps agent that you can query conversationally ("what's our current CPU usage?") but also monitors your infrastructure continuously, detecting anomalies and starting remediation before you even know there's a problem. The Challenge: Real-Time Change Detection Here's where building ambient agents gets tricky. While chat-based agents work perfectly within the request-response paradigm, ambient agents need something entirely different: continuous monitoring and real-time change detection. How do you efficiently detect changes across multiple data sources? How do you avoid the performance nightmare of constant polling? How do you ensure your agent reacts instantly when something critical happens? Developers trying to build ambient agents hit the same wall: creating a reliable, scalable change detection system is hard. You either end up with: Polling hell: Constantly querying databases, burning through resources, and still missing changes between polls Legacy system rewrites: Massive expensive multi-year projects to re-write legacy systems so that they produce domain events Webhook spaghetti: Managing dozens of event sources, each with different formats and reliability guarantees This is where the story takes an interesting turn. Enter Drasi: The Change Detection Engine You Didn't Know You Needed Drasi is not another AI framework. Instead, it solves the problem that ambient agents need solved: intelligent change detection. Think of it as the sensory system for your AI agents, the infrastructure that lets them perceive changes in the world. Drasi is built around three simple components: Sources: Connectivity to the systems that Drasi can observe as sources of change (PostgreSQL, MySQL, Cosmos DB, Kubernetes, EventHub) Continuous Queries: Graph-based queries (using Cypher/GQL) that monitor for specific change patterns Reactions: What happens when a continuous query detects changes, or lack thereof But here's the killer feature: Drasi doesn't just detect that something changed. It understands what changed and why it matters, and even if something should have changed but did not. Using continuous queries, you can define complex conditions that your agents care about, and Drasi handles all the plumbing to deliver those insights in real time. The Bridge: langchain-drasi Integration Now, detecting changes is only part of the challenge. You need to connect those changes to your AI agents in a way that makes sense. That's where langchain-drasi comes in, a purpose-built integration that bridges Drasi's change detection with LangChain's agent frameworks. It achieves this by leveraging the Drasi MCP Reaction, which exposes Drasi continuous queries as MCP resources. The integration provides a simple Tool that agents can use to: Discover available queries automatically Read current query results on demand Subscribe to real-time updates that flow directly into agent memory and workflow Here's what this looks like in practice: from langchain_drasi import create_drasi_tool, MCPConnectionConfig # Configure connection to Drasi MCP server mcp_config = MCPConnectionConfig(server_url="http://localhost:8083") # Create the tool with notification handlers drasi_tool = create_drasi_tool( mcp_config=mcp_config, notification_handlers=[buffer_handler, console_handler] ) # Now your agent can discover and subscribe to data changes # No more polling, no more webhooks, just reactive intelligence The beauty is in the notification handlers: pre-built components that determine how changes flow into your agent's consciousness: BufferHandler: Queues changes for sequential processing LangGraphMemoryHandler: Automatically integrates changes into agent checkpoints LoggingHandler: Integrates with standard logging infrastructure This isn't just plumbing; it's the foundation for what we might call "change-driven architecture" for AI systems. Example: The Seeker Agent Has Entered the Chat Let's make this concrete with my favorite example from the langchain-drasi repository: a hide and seek inspired non-player character (NPC) AI agent that seeks human players in a multi-player game environment. The Scenario Imagine a game where players move around a 2D map, updating their positions in a PostgreSQL database. But here's the twist: the NPC agent doesn't have omniscient vision. It can only detect players under specific conditions: Stationary targets: When a player doesn't move for more than 3 seconds (they're exposed) Frantic movement: When a player moves more than once in less than a second (panicking reveals your position) This creates interesting strategic gameplay, players must balance staying still (safe from detection but vulnerable if found) with moving carefully (one move per second is the sweet spot). The NPC agent seeks based on these glimpses of player activity. These detection rules are defined as Drasi continuous queries that monitor the player positions table. For reference, these are the two continuous queries we will use: When a player doesn't move for more than 3 seconds, this is a great example of detecting the absence of change use the trueLater function: MATCH (p:player { type: 'human' }) WHERE drasi.trueLater( drasi.changeDateTime(p) <= (datetime.realtime() - duration( { seconds: 3 } )), drasi.changeDateTime(p) + duration( { seconds: 3 } ) ) RETURN p.id, p.x, p.y When a player moves more than once in less than a second is an example of using the previousValue function to compare that current state with a prior state: MATCH (p:player { type: 'human' }) WHERE drasi.changeDateTime(p).epochMillis - drasi.previousValue(drasi.changeDateTime(p).epochMillis) < 1000 RETURN p.id, p.x, p.y Here's the neat part: you can dynamically adjust the game's difficulty by adding or removing queries with different conditions; no code changes required, just deploy new Drasi queries. The traditional approach would have your agent constantly polling the data source checking these conditions: "Any player moves? How about now? Now? Now?" The Workflow in Action The agent operates through a LangGraph based state machine with two distinct phases: 1. Setup Phase (First Run Only) Setup queries prompt - Prompts the AI model to discover available Drasi queries Setup queries call model - AI model calls the Drasi tool with discover operation Setup queries tools - Executes the Drasi tool calls to subscribe to relevant queries This phase loops until the AI model has discovered and subscribed to all relevant queries 2. Main Seeking Loop (Continuous) Check sensors - Consumes any new Drasi notifications from the buffer into the workflow state Evaluate targets - Uses AI model to parse sensor data and extract target positions Select and plan - Selects closest target and plans path Execute move - Executes the next move via game API Loop continues indefinitely, reacting to new notifications No polling. No delays. No wasted resources checking positions that don't meet the detection criteria. Just pure, reactive intelligence flowing from meaningful data changes to agent actions. The continuous queries act as intelligent filters, only alerting the agent when relevant changes occur. Click here for the full implementation The Bigger Picture: Change-Driven Architecture What we're seeing with Drasi and ambient agents isn't just a new tool, it's a new architectural pattern for AI systems. The core idea is profound: AI agents can react to the world changing, not just wait to be asked about it. This pattern enables entirely new categories of applications that complement traditional chat interfaces. The example might seem playful, but it demonstrates that AI agents can perceive and react to their environment in real time. Today it's seeking players in a game. Tomorrow it could be: Managing city traffic flows based on real-time sensor data Coordinating disaster response as situations evolve Optimizing supply chains as demand patterns shift Protecting networks as threats emerge The change detection infrastructure is here. The patterns are emerging. The only question is: what will you build? Where to Go from Here Ready to dive deeper? Here are your next steps: Explore Drasi: Head to drasi.io and discover the power of the change detection platform Try langchain-drasi: Clone the GitHub repository and run the Hide-and-Seek example yourself Join the conversation: The space is new and needs diverse perspectives. Join the community on Discord. Let us know if you have built ambient agents and what challenges you faced with real-time change detection.Foundry Agent Service at Ignite 2025: Simple to Build. Powerful to Deploy. Trusted to Operate.
The upgraded Foundry Agent Service delivers a unified, simplified platform with managed hosting, built-in memory, tool catalogs, and seamless integration with Microsoft Agent Framework. Developers can now deploy agents faster and more securely, leveraging one-click publishing to Microsoft 365 and advanced governance features for streamlined enterprise AI operations.From Policy to Practice: Built-In CIS Benchmarks on Azure - Flexible, Hybrid-Ready
Security is more important than ever. The industry-standard for secure machine configuration is the Center for Internet Security (CIS) Benchmarks. These benchmarks provide consensus-based prescriptive guidance to help organizations harden diverse systems, reduce risk, and streamline compliance with major regulatory frameworks and industry standards like NIST, HIPAA, and PCI DSS. In our previous post, we outlined our plans to improve the Linux server compliance and hardening experience on Azure and shared a vision for integrating CIS Benchmarks. Today, that vision has turned into reality. We're now announcing the next phase of this work: Center for Internet Security (CIS) Benchmarks are now available on Azure for all Azure endorsed distros, at no additional cost to Azure and Azure Arc customers. With today's announcement, you get access to the CIS Benchmarks on Azure with full parity to what’s published by the Center for Internet Security (CIS). You can adjust parameters or define exceptions, tailoring security to your needs and applying consistent controls across cloud, hybrid, and on-premises environments - without having to implement every control manually. Thanks to this flexible architecture, you can truly manage compliance as code. How we achieve parity To ensure accuracy and trust, we rely on and ingest CIS machine-readable Benchmark content (OVAL/XCCDF files) as the source of truth. This guarantees that the controls and rules you apply in Azure match the official CIS specifications, reducing drift and ensuring compliance confidence. What’s new under the hood At the core of this update is azure-osconfig’s new compliance engine - a lightweight, open-source module developed by the Azure Core Linux team. It evaluates Linux systems directly against industry-standard benchmarks like CIS, supporting both audit and, in the future, auto-remediation. This enables accurate, scalable compliance checks across large Linux fleets. Here you can read more about azure-osconfig. Dynamic rule evaluation The new compliance engine supports simple fact-checking operations, evaluation of logic operations on them (e.g., anyOf, allOf) and Lua based scripting, which allows to express complex checks required by the CIS Critical Security Controls - all evaluated natively without external scripts. Scalable architecture for large fleets When the assignment is created, the Azure control plane instructs the machine to pull the latest Policy package via the Machine Configuration agent. Azure-osconfig’s compliance engine is integrated as a light-weight library to the package and called by Machine Configuration agent for evaluation – which happens every 15-30minutes. This ensures near real-time compliance state without overwhelming resources and enables consistent evaluation across thousands of VMs and Azure Arc-enabled servers. Future-ready for remediation and enforcement While the Public Preview starts with audit-only mode, the roadmap includes per-rule remediation and enforcement using technologies like eBPF for kernel-level controls. This will allow proactive prevention of configuration drift and runtime hardening at scale. Please reach out if you interested in auto-remediation or enforcement. Extensibility beyond CIS Benchmarks The architecture was designed to support other security and compliance standards as well and isn’t limited to CIS Benchmarks. The compliance engine is modular, and we plan to extend the platform with STIG and other relevant industry benchmarks. This positions Azure as a platform for a place where you can manage your compliance from a single control-plane without duplicating efforts elsewhere. Collaboration with the CIS This milestone reflects a close collaboration between Microsoft and the CIS to bring industry-standard security guidance into Azure as a built-in capability. Our shared goal is to make cloud-native compliance practical and consistent, while giving customers the flexibility to meet their unique requirements. We are committed to continuously supporting new Benchmark releases, expanding coverage with new distributions and easing adoption through built-in workflows, such as moving from your current Benchmark version to a new version while preserving your custom configurations. Certification and trust We can proudly announce that azure-osconfig has met all the requirements and is officially certified by the CIS for Benchmark assessment, so you can trust compliance results as authoritative. Minor benchmark updates will be applied automatically, while major version will be released separately. We will include workflows to help migrate customizations seamlessly across versions. Key Highlights Built-in CIS Benchmarks for Azure Endorsed Linux distributions Full parity with official CIS Benchmarks content and certified by the CIS for Benchmark Assessment Flexible configuration: adjust parameters, define exceptions, tune severity Hybrid support: enforce the same baseline across Azure, on-prem, and multi-cloud with Azure Arc Reporting format in CIS tooling style Supported use cases Certified CIS Benchmarks for all Azure Endorsed Distros - Audit only (L1/L2 server profiles) Hybrid / On-premises and other cloud machines with Azure Arc for the supported distros Compliance as Code (example via Github -> Azure OIDC auth and API integration) Compatible with GuestConfig workbook What’s next? Our next mission is to bring the previously announced auto-remediation capability into this experience, expand the distribution coverage and elevate our workflows even further. We’re focused on empowering you to resolve issues while honoring the unique operational complexity of your environments. Stay tuned! Get Started Documentation link for this capability Enable CIS Benchmarks in Machine Configuration and select the “Official Center for Internet Security (CIS) Benchmarks for Linux Workloads” then select the distributions for your assignment, and customize as needed. In case if you want any additional distribution supported or have any feedback for azure-osconfig – please open an Azure support case or a Github issue here Relevant Ignite 2025 session: Hybrid workload compliance from policy to practice on Azure Connect with us at Ignite Meet the Linux team and stop by the Linux on Azure booth to see these innovations in action: Session Type Session Code Session Name Date/Time (PST) Theatre THR 712 Hybrid workload compliance from policy to practice on Azure Tue, Nov 18/ 3:15 PM – 3:45 PM Breakout BRK 143 Optimizing performance, deployments, and security for Linux on Azure Thu, Nov 20/ 1:00 PM – 1:45 PM Breakout BRK 144 Build, modernize, and secure AKS workloads with Azure Linux Wed, Nov 19/ 1:30 PM – 2:15 PM Breakout BRK 104 From VMs and containers to AI apps with Azure Red Hat OpenShift Thu, Nov 20/ 8:30 AM – 9:15 AM Theatre THR 701 From Container to Node: Building Minimal-CVE Solutions with Azure Linux Wed, Nov 19/ 3:30 PM – 4:00 PM Lab Lab 505 Fast track your Linux and PostgreSQL migration with Azure Migrate Tue, Nov 18/ 4:30 PM – 5:45 PM PST Wed, Nov 19/ 3:45 PM – 5:00 PM PST Thu, Nov 20/ 9:00 AM – 10:15 AM PSTObservability for Multi-Agent Systems with Microsoft Agent Framework and Azure AI Foundry
Agentic applications are revolutionizing enterprise automation, but their dynamic toolchains and latent reasoning make them notoriously hard to operate. In this post, you'll learn how to instrument a Microsoft Agent Framework–based service with OpenTelemetry, ship traces to Azure AI Foundry observability, and adopt a practical workflow to debug, evaluate, and improve multi-agent behavior in production. We'll show how to wire spans around reasoning steps and tool calls (OpenAPI / MCP), enabling deep visibility into your agentic workflows. Who Should Read This? Developers building agents with Microsoft Agent Framework (MAF) in .NET or Python Architects/SREs seeking enterprise-grade visibility, governance, and reliability for deployments on Azure AI Foundry Why Observability Is Non-Negotiable for Agents Traditional logs fall short for agentic systems: Reasoning and routing (which tool? which doc?) are opaque without explicit spans/events Failures often occur between components (e.g., retrieval mismatch, tool schema drift) Without traces across agents ⇄ tools ⇄ data stores, you can't reproduce or evaluate behavior Microsoft has introduced multi-agent observability patterns and OpenTelemetry (OTel) conventions that unify traces across Agent Framework, Foundry, and popular stacks—so you can see one coherent timeline for each task. Reference Architecture Key Capabilities Agent orchestration & deployment via Microsoft Agent Framework Model access using Foundry’s OpenAI-compatible endpoint OpenTelemetry for traces/spans + attributes (agent, tool, retrieval, latency, tokens) Step-by-Step Implementation Assumption: This article uses Azure Monitor (via Application Insights) as the OpenTelemetry exporter, but you can configure other supported exporters in the same way. Prerequisites .NET 8 SDK or later Azure OpenAI service (endpoint, API key, deployed model) Application Insights and Grafana Create an Agent with OpenTelemetry (ASP.NET Core or Console App) Install required packages: dotnet add package Azure.AI.OpenAI dotnet add package Azure.Monitor.OpenTelemetry.Exporter dotnet add package Microsoft.Agents.AI.OpenAI dotnet add package Microsoft.Extensions.Logging dotnet add package OpenTelemetry dotnet add package OpenTelemetry.Trace dotnet add package OpenTelemetry.Metrics dotnet add package OpenTelemetry.Extensions.Hosting dotnet add package OpenTelemetry.Instrumentation.Http Setup environment variables: AZURE_OPENAI_ENDPOINT: https://<your_service_name>.openai.azure.com/ AZURE_OPENAI_API_KEY: <your_azure_openai_apikey> APPLICATIONINSIGHTS_CONNECTION_STRING: <your_application_insights_connectionstring_for_azuremonitor_exporter> Configure tracing once at startup: var applicationInsightsConnectionString = Environment.GetEnvironmentVariable("APPLICATIONINSIGHTS_CONNECTION_STRING"); // Create a resource describing the service var resource = ResourceBuilder.CreateDefault() .AddService(serviceName: ServiceName) .AddAttributes(new Dictionary<string, object> { ["deployment.environment"] = "development", ["service.instance.id"] = Environment.MachineName }) .Build(); // Setup OpenTelemetry TracerProvider var traceProvider = Sdk.CreateTracerProviderBuilder() .SetResourceBuilder(ResourceBuilder.CreateDefault().AddService(ServiceName)) .AddSource(SourceName) .AddSource("Microsoft.Agents.AI") .AddHttpClientInstrumentation() .AddAzureMonitorTraceExporter(options => { options.ConnectionString = applicationInsightsConnectionString; }) .Build(); // Setup OpenTelemetry MeterProvider var meterProvider = Sdk.CreateMeterProviderBuilder() .SetResourceBuilder(ResourceBuilder.CreateDefault().AddService(ServiceName)) .AddMeter(SourceName) .AddAzureMonitorMetricExporter(options => { options.ConnectionString = applicationInsightsConnectionString; }) .Build(); // Configure DI and OpenTelemetry var serviceCollection = new ServiceCollection(); // Setup Logging with OpenTelemetry and Application Insights serviceCollection.AddLogging(loggingBuilder => { loggingBuilder.SetMinimumLevel(LogLevel.Debug); loggingBuilder.AddOpenTelemetry(options => { options.SetResourceBuilder(ResourceBuilder.CreateDefault().AddService(ServiceName)); options.IncludeScopes = true; options.IncludeFormattedMessage = true; options.AddAzureMonitorLogExporter(exporterOptions => { exporterOptions.ConnectionString = applicationInsightsConnectionString; }); }); loggingBuilder.AddApplicationInsights( configureTelemetryConfiguration: (config) => { config.ConnectionString = Environment.GetEnvironmentVariable("APPLICATIONINSIGHTS_CONNECTION_STRING"); }, configureApplicationInsightsLoggerOptions: options => { options.TrackExceptionsAsExceptionTelemetry = true; options.IncludeScopes = true; }); }); Configure custom metrics and activity source for tracing: using var activitySource = new ActivitySource(SourceName); using var meter = new Meter(SourceName); // Create custom metrics var interactionCounter = meter.CreateCounter<long>("chat_interactions_total", description: "Total number of chat interactions"); var responseTimeHistogram = meter.CreateHistogram<double>("chat_response_time_ms", description: "Chat response time in milliseconds"); 2. Wire-up the AI Agent: // Create OpenAI client var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT"); var apiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY"); var deploymentName = "gpt-4o-mini"; using var client = new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(apiKey)) .GetChatClient(deploymentName) .AsIChatClient() .AsBuilder() .UseOpenTelemetry(sourceName: SourceName, configure: (cfg) => cfg.EnableSensitiveData = true) .Build(); logger.LogInformation("Creating Agent with OpenTelemetry instrumentation"); // Create AI Agent var agent = new ChatClientAgent( client, name: "AgentObservabilityDemo", instructions: "You are a helpful assistant that provides concise and informative responses.") .AsBuilder() .UseOpenTelemetry(SourceName, configure: (cfg) => cfg.EnableSensitiveData = true) .Build(); var thread = agent.GetNewThread(); logger.LogInformation("Agent created successfully with ID: {AgentId}", agent.Id); 3. Instrument Agent logic with semantic attributes and call OpenAI-compatible API: // Create a parent span for the entire agent session using var sessionActivity = activitySource.StartActivity("Agent Session"); Console.WriteLine($"Trace ID: {sessionActivity?.TraceId} "); var sessionId = Guid.NewGuid().ToString("N"); sessionActivity? .SetTag("agent.name", "AgentObservabilityDemo") .SetTag("session.id", sessionId) .SetTag("session.start_time", DateTimeOffset.UtcNow.ToString("O")); logger.LogInformation("Starting agent session with ID: {SessionId}", sessionId); using (logger.BeginScope(new Dictionary<string, object> { ["SessionId"] = sessionId, ["AgentName"] = "AgentObservabilityDemo" })) { var interactionCount = 0; while (true) { Console.Write("You (or 'exit' to quit): "); var input = Console.ReadLine(); if (string.IsNullOrWhiteSpace(input) || input.Equals("exit", StringComparison.OrdinalIgnoreCase)) { logger.LogInformation("User requested to exit the session"); break; } interactionCount++; logger.LogInformation("Processing interaction #{InteractionCount}", interactionCount); // Create a child span for each individual interaction using var activity = activitySource.StartActivity("Agent Interaction"); activity? .SetTag("user.input", input) .SetTag("agent.name", "AgentObservabilityDemo") .SetTag("interaction.number", interactionCount); var stopwatch = Stopwatch.StartNew(); try { logger.LogInformation("Starting agent execution for interaction #{InteractionCount}", interactionCount); var response = await agent.RunAsync(input); Console.WriteLine($"Agent: {response}"); Console.WriteLine(); stopwatch.Stop(); var responseTimeMs = stopwatch.Elapsed.TotalMilliseconds; // Record metrics interactionCounter.Add(1, new KeyValuePair<string, object?>("status", "success")); responseTimeHistogram.Record(responseTimeMs, new KeyValuePair<string, object?>("status", "success")); activity?.SetTag("interaction.status", "success"); logger.LogInformation("Agent interaction #{InteractionNumber} completed successfully in {ResponseTime:F2} seconds", interactionCount, responseTimeMs); } catch (Exception ex) { Console.WriteLine($"Error: {ex.Message}"); Console.WriteLine(); stopwatch.Stop(); var responseTimeMs = stopwatch.Elapsed.TotalSeconds; // Record error metrics interactionCounter.Add(1, new KeyValuePair<string, object?>("status", "error")); responseTimeHistogram.Record(responseTimeMs, new KeyValuePair<string, object?>("status", "error")); activity? .SetTag("response.success", false) .SetTag("error.message", ex.Message) .SetStatus(ActivityStatusCode.Error, ex.Message); logger.LogError(ex, "Agent interaction #{InteractionNumber} failed after {ResponseTime:F2} seconds: {ErrorMessage}", interactionCount, responseTimeMs, ex.Message); } } // Add session summary to the parent span sessionActivity? .SetTag("session.total_interactions", interactionCount) .SetTag("session.end_time", DateTimeOffset.UtcNow.ToString("O")); logger.LogInformation("Agent session completed. Total interactions: {TotalInteractions}", interactionCount); Azure Monitor dashboard Once you run the agent and generate some traffic, your dashboard in Azure Monitor will be populated as shown below: You can drill down to specific service / activity source / spans by applying relevant filters: Key Features Demonstrated OpenTelemetry instrumentation with Microsoft Agent framework Custom metrics for user interactions End-to-end Telemetry correlation Real time telemetry visualization along with metrics and logging interactions Further reading Introducing Microsoft Agent Framework Azure AI Foundry docs OpenTelemetry Aspire Demo with Azure OpenAIWant Safer, Smarter AI? Start with Observability in Azure AI Foundry
Observability in Azure AI: From Black Box to Transparent Intelligence If you are an AI developer or an engineer, you can benefit from Azure AI observability by gaining deep visibility into agent behavior, enabling them to trace decisions, evaluate response quality, and integrate automated testing into their workflows. This empowers you to build safer, more reliable GenAI applications. Responsible AI and compliance teams use observability tools to ensure transparency and accountability, leveraging audit logs, policy mapping, and risk scoring. These capabilities help organizations align AI development with ethical standards and regulatory requirements. Understanding Observability Imagine you're building a customer support chatbot using Azure AI. It’s designed to answer billing questions, troubleshoot issues, and escalate complex cases to human agents. Everything works well in testing—but once deployed, users start reporting confusing answers and slow response times. Without observability, you’re flying blind. You don’t know: Which queries are failing. Why the chatbot is choosing certain responses. Whether it's escalating too often or not enough. How latency and cost are trending over time. Enter Observability: With Azure AI Foundry and Azure Monitor, you can: Trace every interaction: See the full reasoning path the chatbot takes—from user input to model invocation to tool calls. Evaluate response quality: Automatically assess whether answers are grounded, fluent, and relevant. Monitor performance: Track latency, throughput, and cost per interaction. Detect anomalies: Use Azure Monitor’s ML-powered diagnostics to spot unusual patterns. Improve continuously: Feed evaluation results back into your CI/CD pipeline to refine the chatbot with every release. This is observability in action: turning opaque AI behavior into transparent, actionable insights. It’s not just about fixing bugs—it’s about building AI you can trust. Next, let’s understand more about observability: What Is Observability in Azure AI? Observability in Azure AI refers to the ability to monitor, evaluate, and govern AI agents and applications across their lifecycle—from model selection to production deployment. It’s not just about uptime or logs anymore. It’s about trust, safety, performance, cost, and compliance. Observability aligned with the end-to-end AI application development workflow. Image source: Microsoft Learn Key Components and Capabilities Azure AI Foundry Observability Built-in observability for agentic workflows. Tracks metrics like performance, quality, cost, safety, relevance, and “groundedness” in real time. Enables tracing of agent interactions and data lineage. Supports alerts for risky or off-policy responses and integrates with partner governance platforms. Find details on Observability here: Observability in Generative AI with Azure AI Foundry - Azure AI Foundry | Microsoft Learn AI Red Teaming (PyRIT Integration) Scans agents for safety vulnerability. Evaluates attack success rates across categories like hate, violence, sexual content, and l more. Generates scorecards and logs results in the Foundry portal. Find details here: AI Red Teaming Agent - Azure AI Foundry | Microsoft Learn Image source: Microsoft Learn CI/CD Integration GitHub Actions and Azure DevOps workflows automate evaluations. Continuous monitoring and regression detection during development Azure Monitor + Azure BRAIN Uses ML and LLMs for anomaly detection, forecasting, and root cause analysis. Offers multi-tier log storage (Gold, Silver, Bronze) with unified KQL query experience. Integrates with Azure Copilot for diagnostics and optimization. Open Telemetry Extensions Azure is extending OTel with agent-specific entities like AgentRun, ToolCall, Eval, and ModelInvocation. Enables fleet-scale dashboards and semantic tracing for GenAI workloads. Observability as a First-Class Citizen in Azure AI Foundry In Azure AI Foundry, observability isn’t bolted on—it’s built in. The platform treats observability as a first-class capability, essential for building trustworthy, scalable, and responsible AI systems. Image source: Microsoft Learn What Does This Mean in Practice? Semantic Tracing for Agents Azure AI Foundry enables intelligent agents to perform tasks using AgentRun, ToolCall, and ModelInvocation. AgentRun manages the entire lifecycle of an agent's execution, from input processing to output generation. ToolCall allows agents to invoke external tools or APIs for specific tasks, like fetching data or performing calculations. ModelInvocation lets agents directly use AI models for advanced tasks, such as sentiment analysis or image recognition. Together, these components create adaptable agents capable of handling complex workflows efficiently. Integrated Evaluation Framework Developers can continuously assess agent responses for quality, safety, and relevance using built-in evaluators. These can be run manually or automatically via CI/CD pipelines, enabling fast iteration and regression detection. Governance and Risk Management Observability data feeds directly into governance workflows. Azure AI Foundry supports policy mapping, risk scoring, and audit logging, helping teams meet compliance requirements while maintaining agility. Feedback Loop for Continuous Improvement Observability isn’t just about watching—it’s about learning. Azure AI Foundry enables teams to use telemetry and evaluation data to refine agents, improve performance, and reduce risk over time. Now, Build AI You Can Trust Observability isn’t just a technical feature—it’s the foundation of responsible AI. Whether you're building copilots, deploying GenAI agents, or modernizing enterprise workflows, Azure AI Foundry and Azure Monitor give you the tools to trace, evaluate, and improve every decision your AI makes. Now is the time to move beyond black-box models and embrace transparency, safety, and performance at scale. Start integrating observability into your AI workflows and unlock the full potential of your agents—with confidence. Read more here: Plans | Microsoft Learn Observability and Continuous Improvement - Training | Microsoft Learn Observability in Generative AI with Azure AI Foundry - Azure AI Foundry | Microsoft Learn About the Author Priyanka is a Technical Trainer at Microsoft USA with over 15 years of experience as a Microsoft Certified Trainer. She has a profound passion for learning and sharing knowledge across various domains. Priyanka excels in delivering training sessions, proctoring exams, and upskilling Microsoft Partners and Customers. She has significantly contributed to AI and Data-related courseware, exams, and high-profile events such as Microsoft Ignite, Microsoft Learn Live Shows, MCT Community AI Readiness, and Women in Cloud Skills Ready. Furthermore, she supports initiatives like “Code Without Barrier” and “Women in Azure AI,” contributing to AI Skills enhancements. Her primary areas of expertise include courses on Development, Data, and AI. In addition to maintaining and acquiring new certifications in Data and AI, she has also guided learners and enthusiasts on their educational journeys. Priyanka is an active member of the Microsoft Tech community, where she reviews and writes blogs focusing on Data and AI. #SkilledByMTT #MSLearn #MTTBloggingGroup
