# telemetry Published articles for telemetry. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Behind the Scenes: How the OpenTelemetry Plugin Maps Your Microservices in Real-Time DevFeed: [Behind the Scenes: How the OpenTelemetry Plugin Maps Your Microservices in Real-Time]() Original publisher: [Read original article]() Author: Egor Klimov Published: 2026-09-16T12:34:47Z Content type: article Language: en Sources: [The JetBrains Blog]() Topics: [OpenTelemetry](), [Microservices](), [observability](), [telemetry](), [Traces](), [ide]() Tags: [all-things-web](), [backstage](), [environment-variables](), [goland](), [ide](), [idea](), [intellij-idea](), [intellij-platform](), [jetbrains](), [microservices](), [observability](), [opentelemetry](), [performance-optimization](), [plugin-development](), [plugin-highlights](), [plugins](), [pycharm](), [research](), [rider](), [telemetry](), [traces](), [webstorm]() ### AI overview This article explains how the JetBrains OpenTelemetry Plugin generates a service map from runtime telemetry. It describes using logs, metrics, and especially standardized trace spans to visualize how microservices communicate, along with the plugin's lightweight local OpenTelemetry backend. ### Source excerpt We've all been there: you join a new project, and the first thing you ask for is the architecture diagram. You're handed a diagram that looks great, but after a week of debugging, you realize it's six months out of date. Service A hasn't talked to Service B since the spring, and there's a new [...] ## Kubernetes attributes processor reaches v1.0.0 milestone DevFeed: [Kubernetes attributes processor reaches v1.0.0 milestone]() Original publisher: [Read original article]() Author: OpenTelemetry Authors; Docs CC BY Published: 2026-09-16T04:59:27Z Content type: release Language: en Sources: [Blog on OpenTelemetry]() Topics: [OpenTelemetry](), [telemetry](), [releases](), [Kubernetes](), [Go Language]() Tags: [api](), [go](), [kubernetes](), [opentelemetry](), [releases](), [stable](), [telemetry]() ### AI overview The Kubernetes attributes processor has reached version 1.0.0 and fulfills OpenTelemetry's stability criteria for testing, benchmarking, documentation, and telemetry. The release also supports redistribution as a Go library or in binaries without API breakage. ### Source excerpt The Kubernetes attributes processor, which enriches your telemetry with Kubernetes metadata, has officially moved to v1.0.0! You can try it out on your custom distro, and it is also available as part of the latest opentelemetry-collector-contrib and opentelemetry-collector-k8s distro releases. Being v1.0.0 means the component is now verified to fulfill the 'stable' stability criteria including requirements around testing, benchmarking, documentation and telemetry stability. It also ensures you can redistribute it as a Go library or as part of your binaries without API breakage. ## From alert to resolution: Manage incidents with Bits Chat in Slack DevFeed: [From alert to resolution: Manage incidents with Bits Chat in Slack]() Original publisher: [Read original article]() Author: Nancy Zhu; Evan Marcantonio; Nicole Parisi; Chris Miller Published: 2026-09-16T00:00:00Z Content type: tutorial Language: en Sources: [Datadog | The Monitor blog]() Topics: [incident](), [Slack](), [Artificial Intelligence](), [telemetry](), [Code](), [Pull Request]() Tags: [ai](), [also](), [bits-ai](), [code](), [collaboration](), [ecommerce](), [incident](), [monitor](), [root-cause-analysis](), [slack](), [telemetry]() ### AI overview This tutorial explains how Bits Chat in Slack can support incident response by investigating alerts, analyzing telemetry, coordinating responders, generating a pull request for a fix, and tracking follow-up work within the incident channel. ### Source excerpt Use Bits Chat in Slack to investigate incidents, collaborate with responders, make code fixes, and capture follow-up work where your team communicates. ## Transform and route security logs to Microsoft Sentinel tables using Observability Pipelines DevFeed: [Transform and route security logs to Microsoft Sentinel tables using Observability Pipelines]() Original publisher: [Read original article]() Author: Zara Boddula; Danielle Park Published: 2026-09-16T00:00:00Z Content type: tutorial Language: en Sources: [Datadog | The Monitor blog]() Topics: [observability pipelines](), [SIEM, Security](), [Security](), [log management](), [Microsoft]() Tags: [azure](), [cisco-meraki](), [devsecops](), [fortigate](), [log-management](), [logs](), [observability-pipelines](), [pipelines](), [security](), [siem](), [telemetry](), [threat-detection]() ### AI overview Datadog's Observability Pipelines Packs transform firewall, VPN, and network detection logs into Microsoft Sentinel table schemas before ingestion. The post describes Packs for Palo Alto Networks, Fortinet, Cisco ASA, Cisco Meraki, and ExtraHop, including filtering and noise reduction to help control Sentinel ingest volume while retaining visibility. ### Source excerpt Learn how Observability Pipelines Packs map security logs to Microsoft Sentinel schemas and help control downstream ingest volume. ## Agents operate, humans govern: Scale your operations and reduce toil with Azure SRE Agent DevFeed: [Agents operate, humans govern: Scale your operations and reduce toil with Azure SRE Agent]() Original publisher: [Read original article]() Author: TNS Staff Published: 2026-09-15T16:21:45Z Content type: article Language: en Sources: [The New Stack]() Topics: [SRE](), [incident](), [Azure](), [observability](), [DevOps](), [Redis]() Tags: [agent](), [ai-agents](), [ai-operations](), [azure](), [code-review](), [deployment](), [devops](), [incident](), [microsoft-azure](), [monitoring](), [observability](), [post](), [redis](), [sponsor-microsoft-azure](), [sponsored](), [sponsored-post](), [sre](), [telemetry]() ### AI overview The article describes Azure SRE Agent as a system that analyzes telemetry, correlates deployment and monitoring data, investigates incidents, identifies root causes, recommends or prepares fixes, and supports mitigation and other operational tasks under human approval. It cites examples involving Microsoft service teams and InEight, including a recommendation to scale Redis. ### Source excerpt What if engineers could spend their time building and optimizing systems rather than maintaining them? It's 3 a.m., and the The post Agents operate, humans govern: Scale your operations and reduce toil with Azure SRE Agent appeared first on The New Stack. ## Atlassian Automates Root Cause Analysis by Correlating Metrics, Logs and Traces DevFeed: [Atlassian Automates Root Cause Analysis by Correlating Metrics, Logs and Traces]() Original publisher: [Read original article]() Author: Craig Risi Published: 2026-09-15T12:00:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [incident](), [atlassian](), [telemetry](), [SIEM, Security, Observability](), [tracing](), [Cloud Native Ecosystem](), [OpenTelemetry]() Tags: [atlassian](), [atlassian-automated-rca](), [cloud-native](), [defects](), [devops](), [distributed](), [incident](), [incident-response](), [logging](), [logs](), [metrics](), [news](), [opentelemetry](), [root-cause-analysis](), [services](), [telemetry](), [traces](), [tracing]() ### AI overview Atlassian has outlined an approach to automating root cause analysis for large-scale cloud-native incidents. It correlates metrics, logs, distributed traces, and service topology to detect anomalies, align them in time, trace dependencies, and produce ranked hypotheses about likely fault origins and propagation paths. ### Source excerpt Atlassian has outlined a new approach to automating root cause analysis for large-scale cloud-native incidents, using correlation across metrics, logs, distributed traces, and service topology to generate ranked hypotheses about where failures originate and how they propagate. By Craig Risi ## Closing the Resilience Gap with Native Splunk in Cisco Nexus One DevFeed: [Closing the Resilience Gap with Native Splunk in Cisco Nexus One]() Original publisher: [Read original article]() Author: Murali Gandluru Published: 2026-09-14T19:55:29Z Content type: article Language: en Sources: [Cisco Blogs]() Topics: [Resilience](), [telemetry](), [Network](), [dashboards](), [Architecture & Design](), [data](), [Artificial Intelligence]() Tags: [2026](), [ai](), [architecture](), [business](), [cisco](), [cisco-data-fabric](), [dashboards](), [data](), [data-center](), [infrastructure](), [network](), [nexus-dashboard](), [nexus-one](), [operational](), [resilience](), [telemetry]() ### AI overview Cisco describes expanding Native Splunk in Cisco Nexus One from a single-node to a multi-node architecture. The integration brings Splunk search, dashboards, alerting, and analytics together with authoritative network context and distributed application, infrastructure, and security data through Cisco Data Fabric, helping teams investigate incidents and move from telemetry to root cause faster. ### Source excerpt See how Native Splunk and Cisco Nexus One bring analytics closer to network data, strengthen resilience, and connect teams across operational domains. ## Visual playback of the user journey: Introducing Session Replay in Grafana Cloud Frontend Observability DevFeed: [Visual playback of the user journey: Introducing Session Replay in Grafana Cloud Frontend Observability]() Original publisher: [Read original article]() Author: Lukasz Gut Published: 2026-09-12T11:22:06.456390Z Content type: article Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [session replay](), [Grafana Cloud Frontend Observability](), [Frontend observability](), [web applications](), [telemetry](), [SDKs]() Tags: [analytics](), [frontend-observability](), [grafana](), [grafana-cloud-frontend-observability](), [instrumentation](), [logs](), [metrics](), [monitoring](), [observability](), [privacy](), [session-replay](), [telemetry](), [traces](), [web-applications]() ### AI overview The article introduces Session Replay, a public-preview add-on for Grafana Cloud Frontend Observability. It visually reconstructs how a web application appeared and changed during a user session, linking that playback to session timelines and technical telemetry so engineering teams can investigate frontend problems more directly. ### Source excerpt Grafana Cloud Frontend Observability helps engineering teams quantify the end user experience by bringing metrics, logs, traces, and user session context to client-side web applications. Teams can monitor application health and performance over time, triage errors, and correlate frontend signals with backend telemetry to investigate issues across the stack. Yet some of the hardest frontend problems remain difficult to diagnose. A support ticket might report that a checkout button did nothing, a form unexpectedly reset, or a workflow broke only in one browser or on one device. Metrics can reveal a performance regression, logs can capture an error, and traces can expose a slow request, but no single signal shows what the interface actually looked like to the user. This is exactly why we built Session Replay, an add-on feature in Frontend Observability that provides a visual reconstruction of a user's journey, connected to the telemetry Grafana Cloud already collects. It helps engineering teams move from a reported problem to seeing what happened and knowing exactly where to investigate next. What is Session Replay in Grafana Cloud Frontend Observability? Session Replay, now in public preview, adds visual playback capabilities to Frontend Observability. It reconstructs how a web application appeared and changed as a user navigated and interacted with it, so you can observe the journey as it unfolded instead of inferring it from individual telemetry events. Frontend Observability already brings together a chronological timeline of the events within a user session. That timeline tells you what happened and when. Session Replay adds the missing visual context: what was happening in the interface around those events and how one interaction led to the next. Because both views belong to the same session, you can move between the user experience and the relevant technical signals without losing the thread of your investigation. Session Replay builds on the Grafana Faro Web SD ## Knowledge Graph as context for LLMs: demonstrating decisive RCA and faster production performance DevFeed: [Knowledge Graph as context for LLMs: demonstrating decisive RCA and faster production performance]() Original publisher: [Read original article]() Author: Sarah Constant Published: 2026-09-12T11:22:06.456390Z Content type: article Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [Large Language Model](), [Grafana Cloud](), [debugging](), [incident](), [telemetry](), [observability ai agents]() Tags: [ai](), [ai-agents](), [analytics](), [debugging](), [grafana](), [grafana-cloud](), [incident](), [knowledge-graph](), [llms](), [logs](), [metrics](), [monitoring](), [observability](), [telemetry](), [traces]() ### AI overview The article reports Grafana Labs experiments testing whether providing AI agents with Grafana Cloud's Knowledge Graph improves incident debugging compared with raw telemetry alone. In one incident replayed 16 times per approach, Knowledge Graph context led to the correct root cause 15 times, versus once with raw telemetry. The article also describes challenges including misleading signals, unsupported confident answers, and inconsistent investigations, arguing that well-structured context matters more than context-window size alone. ### Source excerpt On the product team here at Grafana Labs, we consider AI agents our users, too. That's why we set out to test how well agents can debug incidents across the full stack, and how much better they perform with Grafana Cloud's Knowledge Graph vs. using raw telemetry alone. Our early results are promising. In one real incident we replayed 16 times each way, an agent with Knowledge Graph context found the correct root cause 15 times, compared with just once using raw telemetry alone. Along the way, we also uncovered some of the challenges that still stand in the way of reliable AI-assisted debugging, from chasing the wrong signals to confidently making things up and producing inconsistent answers. We're still early, but our findings point to an important idea. The industry's shorthand right now is that a bigger context window will lead to better outputs. Our findings suggest it's not just about more context; it's about structuring your data well enough to serve the right context. Here's a look at what we've learned so far, as we continue to experiment out in the open and bring you along, the Grafana Labs way. Giving an agent access to telemetry is just the beginning Give a current-generation model like Opus 4.8 access to your raw telemetry during a live incident, and it genuinely starts to figure things out: querying metrics and logs, forming a hypothesis, and checking it. We have watched it work on our own incidents, and it does it affordably. But if you run software at scale, where uptime is business-critical and large teams share the responsibility, a better model alone doesn't get you all the way there for debugging. From analyzing how LLMs do root-cause analysis on our own infrastructure, and from speaking to our customers, we've uncovered three problems that get in the way: The further the cause is from the alert, the more likely the model is to get it wrong. An agent may confidently make things up when it doesn't have the evidence it needs. The same investigation c ## Custom labels in Grafana Cloud Synthetic Monitoring: New updates for consistency and ease-of-use DevFeed: [Custom labels in Grafana Cloud Synthetic Monitoring: New updates for consistency and ease-of-use]() Original publisher: [Read original article]() Author: Anant Sharma Published: 2026-09-12T11:22:06.456390Z Content type: article Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [Grafana Cloud](), [synthetic monitoring](), [Grafana](), [Monitoring](), [telemetry](), [dashboards]() Tags: [analytics](), [dashboards](), [grafana](), [grafana-cloud](), [how-to](), [logs](), [metrics](), [monitoring](), [observability](), [synthetic-monitoring](), [telemetry](), [traces](), [update]() ### AI overview Grafana Cloud Synthetic Monitoring is updating custom labels so they attach directly to every check metric and log, rather than only sm_check_info. The label_ prefix will be removed, and labels will appear exactly as defined. Existing users must migrate dashboards, SLOs, alerts, and queries that reference these labels by March 1, 2027. ### Source excerpt Labels are a powerful way to organize telemetry and define policies across Grafana Cloud, helping to streamline alerting, attribution, access control, and more. But traditionally, custom labels in Synthetic Monitoring have worked a little differently: they only lived on a single sm_check_info metric, and Grafana Cloud prefixed each one with label_. To make custom labels in Synthetic Monitoring work consistently with the rest of Grafana Cloud--without extra joins, naming conventions, or workarounds--we're rolling out an update that lets your custom labels attach directly to every check metric, not just sm_check_info, and removes the label_ prefix. Starting today, labels appear exactly as you write them, making Synthetic Monitoring data easier to navigate and use with label-based policies across Grafana Cloud. If you currently use custom labels in Synthetic Monitoring, read on to learn how to migrate to the new labels. We are asking users to migrate by March 1, 2027 to ensure their custom dashboards, SLOs, alerts, and queries that reference Synthetic Monitoring metrics do not break, and continue to work as expected. If you do not use custom labels in Synthetic Monitoring, you don't need to do anything to prepare for this update. How custom labels work in Synthetic Monitoring Until now, if you wanted to filter a dashboard, scope an alert, or attribute cost by team or service within Synthetic Monitoring, you had to join sm_check_info against the check metric you actually want to query. You also had to remember that team is really label_team in this context. That approach worked to ensure your custom labels were never at odds with system-defined labels. However, it broke down as usage scaled up and dozens of teams started running hundreds of checks across services, environments, and regions. Teams rely on consistent schemas to direct label-based workflows, and this update brings Synthetic Monitoring further into the fold of your existing policies. With the update, labels i ## How to scale Alloy as a central telemetry gateway: capacity planning, load testing, and production lessons DevFeed: [How to scale Alloy as a central telemetry gateway: capacity planning, load testing, and production lessons]() Original publisher: [Read original article]() Author: Fatjon Nebiu Published: 2026-09-12T11:22:06.456390Z Content type: tutorial Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [Grafana]() Tags: [analytics](), [auth](), [best-practices](), [cloud](), [cost](), [data](), [deployment](), [enterprise](), [grafana](), [grafana-alloy](), [grafana-cloud](), [how-to](), [kubernetes](), [logs](), [metrics](), [monitoring](), [observability](), [opentelemetry](), [platform](), [production](), [prometheus](), [scale](), [telemetry](), [testing](), [traces]() ### AI overview A practical guide to scaling Grafana Alloy as a centralized telemetry gateway. It covers capacity planning, load testing, and production considerations for collecting metrics, logs, and traces and forwarding them to Grafana Cloud. ### Source excerpt Running Alloy as a single-instance sidecar is simple. Running it as a centralized gateway that absorbs the full telemetry stream of an enterprise platform--tens of millions of active series, terabytes of logs per day, and tens of thousands of trace spans per second--is a different challenge altogether. To get it right, you need deliberate capacity planning, honest load testing, and a monitoring setup that doesn't rely on the very thing you're testing. As part of the Professional Services team here at Grafana Labs, we've seen this firsthand working with customers. In this post, we'll walk you through the best practices we follow to help them find success, and we'll do so using real, anonymized data from a recent engagement. We'll cover how we sized and load tested a production Alloy central collector deployment on Kubernetes, what the numbers looked like under real stress, and how the cluster behaves today handling the full production telemetry workload for a large enterprise platform. By the end, you should have a better sense for how you can create your own central gateway for collecting telemetry in Grafana Cloud. Why a central gateway? Before diving into numbers, it's worth explaining the pattern. In a central gateway setup, all telemetry from application teams--metrics, logs, and traces--flows to a shared Alloy fleet via OTLP or native Prometheus/Loki write protocols. Alloy buffers, processes, batches, and forwards everything to Grafana Cloud. This gives you several things that per-team sidecar deployments struggle to provide: A single control plane: Auth, rate limiting, and routing in one place so application teams don't need to manage Grafana Cloud credentials Centralized buffering: Ensure a transient Grafana Cloud slowdown doesn't immediately cause data loss at the source Cost visibility: Configure the gateway to only accept telemetry data containing the label or attribute that is mandatory for cost-attribution Protocol normalization: Send OTLP, Prometheus Remote ## CrowdStrike Named Strongest Overall Leader in 2026 Frost Radar™: Cloud Workload Protection Platforms DevFeed: [CrowdStrike Named Strongest Overall Leader in 2026 Frost Radar™: Cloud Workload Protection Platforms]() Original publisher: [Read original article]() Author: Brett Shaw Published: 2026-09-12T11:17:51.295154Z Content type: article Language: en Sources: [Blog]() Topics: [workload protection](), [Security & compliance, Cloud security](), [Security](), [telemetry](), [Containers](), [Kubernetes](), [SOC]() Tags: [2026](), [cloud](), [cloud-application-security](), [cloud-security](), [containers](), [growth](), [innovation](), [kubernetes](), [real-time](), [security](), [soc](), [telemetry](), [workload-protection]() ### AI overview CrowdStrike says Frost & Sullivan named it the strongest overall leader in the 2026 Frost Radar for Cloud Workload Protection Platforms. The article highlights Falcon Cloud Security's focus on connecting risk, adversary intelligence, and real-time protection across containers, Kubernetes, identities, cloud control planes, endpoints, and SOC operations. ### Source excerpt Falcon Cloud Security earned the highest scores in both Innovation and Growth by connecting risk, adversary intelligence, and real-time protection to stop attacks. ## CrowdStrike Falcon Guardian Defines the Next Generation of AI Security DevFeed: [CrowdStrike Falcon Guardian Defines the Next Generation of AI Security]() Original publisher: [Read original article]() Author: Michael Devins Published: 2026-09-12T11:17:51.295154Z Content type: release Language: en Sources: [Blog]() Topics: [AI Bots]() Tags: [agentic](), [agents](), [ai](), [ai-agent](), [ai-agents](), [ai-gateway](), [ai-security](), [architecture](), [autonomous](), [platform](), [securing-ai](), [security](), [telemetry](), [threat-detection]() ### AI overview CrowdStrike announces Falcon Guardian, an AI detection and response solution for discovering, monitoring, investigating, and securing AI agents at runtime. It adds an AI gateway and connects agent activity with endpoint telemetry to support threat response. ### Source excerpt A new flagship AI detection and response solution delivers runtime protection for AI agents, introduces a new AI gateway, and extends expert-led defense. ## Agent and Model Evaluations in Gemini Enterprise Agent Platform are now GA DevFeed: [Agent and Model Evaluations in Gemini Enterprise Agent Platform are now GA]() Original publisher: [Read original article]() Author: Alex Martin; Dima Melnyk Published: 2026-09-12T11:04:33.891311Z Content type: release Language: en Sources: [Google Developers Blog]() Topics: [ai observability](), [SDKs](), [ci]() Tags: [agent](), [agents](), [ci](), [cli](), [development](), [enterprise](), [evaluation](), [experiments](), [gemini](), [llm](), [metrics](), [model](), [platform](), [production](), [telemetry](), [testing](), [traces]() ### AI overview Gemini Enterprise Agent Platform's evaluation service is generally available. It provides consistent evaluation of agents and models across local experiments and production traffic, with pre-built metrics, adaptive rubrics, custom metrics, simulators, and workflow integrations. ### Source excerpt Agent Platform's evaluation service is now generally available, providing developers with a unified engine to measure agent quality consistently across local development experiments and live production traffic. You can evaluate agents using over 20 pre-built metrics, DeepMind-backed adaptive rubrics, or custom code-based and LLM-as-a-judge metrics stored in a centralized, versioned registry. The service integrates directly into existing workflows via the Agent Platform SDK, agents-cli, and ADK, offering built-in user and environment simulators to automate complex multi-turn testing and streamline CI pipelines. ## Kubernetes v1.37: Native Histograms Graduates to Beta DevFeed: [Kubernetes v1.37: Native Histograms Graduates to Beta]() Original publisher: [Read original article]() Author: Richa Banker Published: 2026-09-11T18:30:00Z Content type: release Language: en Sources: [Kubernetes Blog]() Topics: [telemetry]() Tags: [kubernetes](), [latency](), [metrics](), [observability](), [prometheus](), [storage](), [telemetry](), [time-series]() ### AI overview Kubernetes v1.37 enables Prometheus native histograms by default at Beta. The article explains how dynamic exponential buckets improve metric resolution and quantile accuracy while reducing time-series, scraping, and storage overhead compared with classic histograms. ### Source excerpt I'm excited to announce that native histogram support for Kubernetes metrics is graduating to Beta and is enabled by default in Kubernetes v1.37! Native histograms (previously introduced as Alpha in Kubernetes v1.36 under KEP-5808) bring high-resolution, low-cardinality observability to Kubernetes metrics. By adopting Prometheus Native Histograms, Kubernetes components now expose latency and duration metrics with far greater accuracy while significantly reducing telemetry storage and scraping overhead. Why move beyond classic histograms? Since the early days of Kubernetes observability, duration and latency metrics (such as API server request latencies or scheduling durations) have relied on classic Prometheus histograms. Classic histograms require metric authors to define a static list of cumulative bucket boundaries (le labels), such as 0.005, 0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1, 2.5, 5, 10. While familiar, this approach introduces three major challenges: The Bucket Guessing Game: If a workload's latency profile changes, for example, shifting into microsecond ranges or experiencing long-tail tail latencies beyond the highest bucket, the histogram loses visibility. Specifying bucket boundaries upfront requires knowing the distribution before observing it High Cardinality & Storage Cost: With classic histograms, each bucket boundary is exported as a separate time series (_bucket{le="..."}). A histogram with 10 buckets across multiple labels multiplies the number of time series by 10, increasing memory consumption in Prometheus and inflating time series database (TSDB) storage costs Interpolation Error in Quantiles: Calculating percentiles using histogram_quantile() relies on linear interpolation between static bucket boundaries. When bucket spans are coarse, quantile calculations can suffer from significant estimation error What are Prometheus native histograms? Prometheus Native Histograms replace static user-defined buckets with dynamic, exponential buckets. Inst ## Your Agent Harness Needs Runtime Security DevFeed: [Your Agent Harness Needs Runtime Security]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-09-09T20:59:58Z Content type: tutorial Language: en Sources: [Daily Dose of Data Science]() Topics: [agent observability](), [Agent Harness](), [Security](), [Open Source](), [telemetry]() Tags: [agent](), [agent-harness](), [ai-agents](), [anthropic](), [claude](), [cybersecurity](), [github](), [guide](), [local](), [monitoring](), [open-source](), [security](), [telemetry]() ### AI overview This guide presents Agent Beacon, an open-source telemetry layer for AI agents. It records tool calls, shell commands, file changes, and approval decisions as structured runtime events across supported agent harnesses, providing a live record of agent behavior for security monitoring and investigation. ### Source excerpt A 100% local, open-source guide to recording what AI agents actually do at runtime. ## Why Rider and ReSharper Were Slow to Start, and How Microsoft Helped Fix the Problem DevFeed: [Why Rider and ReSharper Were Slow to Start, and How Microsoft Helped Fix the Problem]() Original publisher: [Read original article]() Author: Alexander Ulitin Published: 2026-09-09T16:45:27Z Content type: article Language: en Sources: [The JetBrains Blog]() Topics: [resharper](), [out-of-process](), [Latency](), [Microsoft](), [telemetry]() Tags: [defender](), [logs](), [microsoft](), [net-tools](), [oop](), [out-of-process](), [performance](), [resharper](), [resharper-oop](), [rider](), [telemetry]() ### AI overview The article investigates slow ReSharper startup after its out-of-process architecture was introduced. Profiling identified Microsoft Defender scanning as the source of substantial first-launch latency, and describes a repeatable investigation tool built with input from Microsoft. ### Source excerpt When we launched ReSharper's out-of-process (OOP) architecture, users reported slower startup times for IDEs using ReSharper on Windows. After profiling, the cause surprised us: Microsoft Defender was scanning our process for longer than we expected. This post is about what we found, what we learned working with Microsoft, and a tool we built that allows [...] ## Honoring #IconsOfQuality: Richard Bradshaw DevFeed: [Honoring #IconsOfQuality: Richard Bradshaw]() Original publisher: [Read original article]() Author: Rajrupa Roychowdhury Published: 2026-09-09T11:44:44Z Content type: article Language: en Sources: [BrowserStack Blog]() Topics: [Testing](), [Software Testing](), [Software Engineering](), [Artificial Intelligence](), [Automation]() Tags: [ai](), [ai-agents](), [automation](), [engineering](), [evals](), [icons-of-quality](), [qa](), [quality-engineering](), [software-engineering](), [software-testing](), [telemetry](), [testing]() ### AI overview BrowserStack profiles Richard Bradshaw, a software testing and quality engineering leader, and discusses his views on AI agents, human-centric automation, and evaluating probabilistic AI systems. ### Source excerpt To celebrate the relentless passion and invaluable contributions of leaders in software quality, BrowserStack is proud to honour Icons of Quality. ## AI-Ready Private Cloud with Cisco and VMware DevFeed: [AI-Ready Private Cloud with Cisco and VMware]() Original publisher: [Read original article]() Author: sabina anja Published: 2026-09-08T15:33:39Z Content type: article Language: en Sources: [VMware Blogs]() Topics: [Artificial Intelligence](), [Network](), [Cloud](), [networking](), [cloud-infrastructure](), [distributed-training](), [datacenter](), [GPU](), [Latency](), [Inference](), [model-deployment](), [inference-endpoints]() Tags: [ai](), [cisco](), [cloud](), [cloud-infrastructure](), [cloud-platform](), [data-center](), [distributed-training](), [fabric](), [gpu](), [home-page](), [inference](), [inference-endpoints](), [latency](), [networking](), [object-storage](), [pipelines](), [private-cloud](), [telemetry](), [vcf-9-1](), [vcf-networking](), [vmware](), [vmware-cloud-foundation]() ### AI overview This article explains why an AI-ready private cloud requires more than adding GPUs. It focuses on how Broadcom and Cisco are integrating VMware Cloud Foundation with Cisco Nexus One Fabric to address AI workload networking, including bandwidth-intensive east-west traffic, bursty north-south traffic, latency, congestion management, and telemetry across virtual and physical infrastructure. ### Source excerpt An AI-ready private cloud is not simply a private cloud with GPUs added to it. What determines whether a private cloud platform can actually serve AI workloads effectively is everything built around them: how the fabric carries traffic, how the tenancy model lets teams consume capacity, and how policy and telemetry stay coherent across the ... Continued The post AI-Ready Private Cloud with Cisco and VMware appeared first on VMware Blogs. ## Vespa Newsletter, September 2026 DevFeed: [Vespa Newsletter, September 2026]() Original publisher: [Read original article]() Author: Bonnie Chase Published: 2026-09-08T00:00:00Z Content type: news Language: en Sources: [Vespa Blog]() Topics: [ann](), [Latency](), [Provisioning](), [Algorithm](), [telemetry](), [Graphs]() Tags: [2026](), [algorithm](), [cloud](), [features](), [graph](), [latency](), [newsletter](), [product](), [provisioning](), [ranking](), [retrieval](), [september-2026](), [telemetry](), [updates]() ### AI overview The September 2026 Vespa newsletter announces updates including time-constrained ANN search, sub-query ranking support, flexible provisioning, new rank features, and telemetry export. It also introduces Vespa.ai Live, an in-person community meetup focused on retrieval and ranking systems. ### Source excerpt Advances in Vespa include time-constrained ANN search, sub-query ranking support, flexible provisioning, new rank features and telemetry export ## Black Hat USA 2026: Building the Agentic SOC, One Live Event at a Time DevFeed: [Black Hat USA 2026: Building the Agentic SOC, One Live Event at a Time]() Original publisher: [Read original article]() Author: Jessica (Bair) Oppenheimer Published: 2026-09-07T15:00:58Z Content type: article Language: en Sources: [Security @ Cisco Blogs]() Topics: [Detection engineering](), [SIEM, Security, Observability](), [Threat Hunting & Intel](), [telemetry](), [NOC](), [Malware]() Tags: [agentic-soc](), [black-hat](), [cisco-secure-access](), [cisco-talos](), [cybersecurity](), [duo](), [firewall](), [malware](), [network-operations-center](), [noc](), [security](), [security-operations-center](), [soc](), [splunk-cloud](), [splunk-enterprise-security](), [telemetry](), [thousandeyes]() ### AI overview Cisco describes its work protecting the Black Hat USA 2026 network alongside NOC leaders and technology partners. The team combined security telemetry and workflows to support visibility, detection engineering, threat hunting, malware analysis, AI protection, and Agentic SOC development. ### Source excerpt Cisco is the Security Cloud Provider for the Black Hat conferences. Learn about the latest innovations for the Agentic SOC. ## Black Hat USA 2026: Safeguarding DNS with Secure Access DevFeed: [Black Hat USA 2026: Safeguarding DNS with Secure Access]() Original publisher: [Read original article]() Author: Steve Vida Published: 2026-09-07T15:00:32Z Content type: article Language: en Sources: [Security @ Cisco Blogs]() Topics: [SIEM, Security, Observability](), [telemetry](), [NOC]() Tags: [apple](), [black-hat](), [cisco-secure-access](), [cisco-security-cloud](), [cisco-talos](), [cisco-xdr](), [cloudflare](), [cybersecurity](), [dns](), [google](), [network-operations-center](), [noc](), [phishing](), [privacy](), [security](), [security-operations-center](), [soc](), [splunk-cloud](), [splunk-enterprise-security](), [statistics](), [telemetry]() ### AI overview Cisco reports on using Secure Access and DNS telemetry to protect the Black Hat USA 2026 network. The article highlights blocking unapproved encrypted DNS resolvers, DNS request statistics, and activity classified as hacking. ### Source excerpt Cisco is the Security Cloud Provider for the Black Hat conferences, over a decade providing DNS Security. Learn about protecting DNS with Secure Access. ## ASCII smuggling crosses over from AI prompt injection to phishing evasion DevFeed: [ASCII smuggling crosses over from AI prompt injection to phishing evasion]() Original publisher: [Read original article]() Author: Microsoft Security Research, Noam Kochavi and Sarah Wolstencroft Published: 2026-09-03T16:00:00Z Content type: article Language: en Sources: [Microsoft Security Blog]() Topics: [ASCII](), [Machine Learning, Security Attacks](), [Language models](), [telemetry](), [Microsoft]() Tags: [ai-models](), [ai-security](), [ascii](), [microsoft](), [phishing](), [security](), [social-engineering](), [telemetry]() ### AI overview Microsoft describes a phishing campaign that uses invisible Unicode tag characters to split lure words and evade email parsing. The technique, known as ASCII smuggling, was previously prominent in AI prompt-injection research because models can process hidden text that people cannot see. ### Source excerpt Invisible Unicode characters popularized for hiding instructions from AI models are now being used to obfuscate words before email filters parse them. The post ASCII smuggling crosses over from AI prompt injection to phishing evasion appeared first on Microsoft Security Blog. ## Counterfeit installers to system compromise: Tracking a deceptive software download campaign DevFeed: [Counterfeit installers to system compromise: Tracking a deceptive software download campaign]() Original publisher: [Read original article]() Author: Microsoft Security Research, Microsoft Defender Experts and Parth Jomadkar Published: 2026-09-01T22:48:28Z Content type: article Language: en Sources: [Microsoft Security Blog]() Topics: [Malware](), [Endpoint Security & XDR](), [C2](), [Microsoft]() Tags: [china](), [defender](), [malware](), [microsoft](), [persistence](), [security](), [techniques](), [telemetry]() ### AI overview Microsoft documents an active malware campaign that uses counterfeit software-download pages and malicious installers to compromise systems. It outlines the attack chain, Defender XDR detection and disruption, and mitigations for blocking untrusted downloads and strengthening endpoint protections. ### Source excerpt An active campaign is impersonating legitimate software vendors to deliver malware through look-alike download pages and regenerated installer archives. Microsoft Defender Experts shares observed attack techniques, Defender XDR detections, indicators of compromise, and practical mitigations to help organizations identify, block, and respond to this threat. The post Counterfeit installers to system compromise: Tracking a deceptive software download campaign appeared first on Microsoft Security Blog. [Next page]()