# telemetry Data emitted by software systems about their behavior, including traces, metrics, and logs. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## CrowdStrike Named a Leader in The Forrester Wave™: External Threat Intelligence Service Providers, Q3 2026 DevFeed: [CrowdStrike Named a Leader in The Forrester Wave™: External Threat Intelligence Service Providers, Q3 2026]() Original publisher: [Read original article]() Author: Counter Adversary Operations Published: 2026-09-18T01:40:54.157408Z Content type: article Language: en Sources: [Blog]() Topics: [Threat Research](), [Security](), [Threat Hunting & Intel](), [Vulnerabilities](), [telemetry](), [Cloud](), [Artificial Intelligence](), [Software as a service]() Tags: [2026](), [cloud](), [infrastructure](), [malware](), [security](), [telemetry](), [threat-hunting-intel](), [threat-intelligence](), [vulnerabilities]() ### AI overview CrowdStrike was named a Leader in Forrester's Q3 2026 External Threat Intelligence Service Providers evaluation, receiving the highest scores for Strength of Offering and Strength of Strategy. The article highlights platform-native intelligence, endpoint telemetry, threat hunting, vulnerability intelligence, malware analysis, and external threat data. ### Source excerpt CrowdStrike has been named a Leader in The Forrester Wave™: External Threat Intelligence Service Providers, Q3 2026, receiving the highest scores in both Strength of Offering and Strength of Strategy. Learn more! ## What is AIOps? DevFeed: [What is AIOps?]() Original publisher: [Read original article]() Author: Databricks Staff Published: 2026-09-17T16:49:43Z Content type: tutorial Language: en Sources: [Databricks]() Topics: [AIOps](), [Artificial Intelligence](), [Monitoring](), [Grafana](), [Microservices](), [telemetry](), [Cloud]() Tags: [ai](), [aiops](), [cloud](), [data-plus-ai-foundations](), [devops](), [logs](), [machine-learning](), [microservices](), [monitoring](), [network](), [observability]() ### AI overview This guide explains AIOps, which applies AI and machine learning to IT operations to detect anomalies, correlate events, identify root causes, and trigger responses. It describes how AIOps analyzes logs, traces, events, and network topology, while complementing observability, DevOps, and human judgment. ### Source excerpt Artificial Intelligence for IT Operations (AIOps) applies AI and machine learning to IT operations to detect anomalies... ## Building an Internal Developer Platform with Artificial Intelligence DevFeed: [Building an Internal Developer Platform with Artificial Intelligence]() Original publisher: [Read original article]() Author: Ben Linders Published: 2026-09-17T11:11:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [internal developer platform](), [AI Development](), [AI search](), [agent observability](), [OpenTelemetry](), [telemetry]() Tags: [agent](), [agents](), [artificial-intelligence](), [culture-methods](), [developer-experience](), [developer-platform](), [distributed-tracing](), [guardrails](), [internal-developer-platform](), [logging](), [logs](), [metrics](), [news](), [opentelemetry](), [platform-artificial-intelligence](), [platform-engineering](), [prometheus](), [traces]() ### AI overview This news article covers a KubeCon presentation about using AI agents as an internal developer platform. It discusses semantic search across sources such as Git, Slack, Jira, repositories, pull requests, and wiki pages; guardrails for controlling actions; and logs, metrics, and traces for understanding agent behavior. The speakers also describe OpenTelemetry conventions for GenAI and related observability tools. ### Source excerpt Agents are becoming the new developer platform, using semantic search with data from tools like Git, Slack, and Jira for context. Things to consider are setting guardrails to block or allow things, and using logs, metrics, and traces to understand agent behavior. By Ben Linders ## OpenTelemetry everywhere: Migrating a metrics platform at scale DevFeed: [OpenTelemetry everywhere: Migrating a metrics platform at scale]() Original publisher: [Read original article]() Author: Iris Grace Endozo, Farzad Vazirnia and Albert Kerr, Atlassian Published: 2026-09-17T11:00:00Z Content type: article Language: en Sources: [Cloud Native Computing Foundation]() Topics: [OpenTelemetry](), [migration](), [observability](), [telemetry](), [atlassian](), [Open Source]() Tags: [atlassian](), [blog](), [collector](), [metrics](), [migration](), [observability](), [opentelemetry](), [pipeline](), [scale](), [sidecar]() ### AI overview Atlassian describes replacing a large-scale gostatsd metrics pipeline with OpenTelemetry while preserving the existing StatsD interface for service teams. The migration uses purpose-built OpenTelemetry Collector distributions across collection, ingest, aggregation, and forwarding stages, with support for both StatsD and OTLP during the transition. ### Source excerpt Why we did this at all For most of the last decade our metrics pipeline ran on gostatsd, the open-source StatsD implementation we maintain. It primarily did two jobs: as sidecar on every host and the... ## Native Splunk brings real-time insights to Cisco Nexus One DevFeed: [Native Splunk brings real-time insights to Cisco Nexus One]() Original publisher: [Read original article]() Author: David Keith Published: 2026-09-16T15:00:59Z Content type: release Language: en Sources: [Cisco Blogs]() Topics: [Nexus Dashboard](), [observability](), [telemetry](), [datacenter](), [Security](), [sensitive data](), [audit](), [configuration]() Tags: [analytics](), [audit](), [cisco](), [cisco-nexus-dashboard](), [configuration](), [data-center](), [data-center-networking](), [network](), [nexus-one](), [observability](), [real-time](), [sensitive-data](), [splunk](), [troubleshooting]() ### AI overview Cisco describes native Splunk embedded in Cisco Nexus Dashboard as an on-premises analytics and observability capability for data center and AI workloads. It processes telemetry locally, correlates network, security, configuration, and audit data, and provides dashboards, searches, and alerts for troubleshooting, data sovereignty, compliance, and cost efficiency. ### Source excerpt Discover how native Splunk embedded in Cisco Nexus Dashboard delivers real-time analytics, faster troubleshooting, and on-premises data sovereignty for modern data center and AI workloads. ## 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. ## ClickHouse Cloud Announces Private Preview of PromQL Support and Time-Series Table Engine DevFeed: [ClickHouse Cloud Announces Private Preview of PromQL Support and Time-Series Table Engine]() Original publisher: [Read original article]() Author: James Cunningham Published: 2026-09-15T14:00:00Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Prometheus](), [Time Series](), [Cloud](), [Grafana](), [telemetry](), [Traces]() Tags: [clickhouse](), [cloud](), [grafana](), [logs](), [metrics](), [prometheus](), [time-series](), [traces]() ### AI overview ClickHouse announces a private preview of PromQL support and a time-series table engine in ClickHouse Cloud, allowing Prometheus metrics to be stored in ClickHouse and queried with existing PromQL. ### Source excerpt ClickHouse PromQL support lets you store Prometheus metrics in ClickHouse Cloud, query them using familiar PromQL, and bring metrics together with your logs and traces without rewriting queries in SQL. ## Cisco and the DISA STIG: Turning Zero Trust Policy into Repeatable Practice - Part 2: Cisco SNA DevFeed: [Cisco and the DISA STIG: Turning Zero Trust Policy into Repeatable Practice - Part 2: Cisco SNA]() Original publisher: [Read original article]() Author: Norman St. Laurent Published: 2026-09-15T13:13:53Z Content type: article Language: en Sources: [Cisco Blogs]() Topics: [Cybersecurity](), [Cisco](), [telemetry](), [Zero Trust](), [vulnerability](), [Network]() Tags: [benchmark](), [cisco](), [cisco-secure-network-analytics-sna](), [cybersecurity](), [department-of-defense-dod](), [government](), [hardening](), [industries](), [nist](), [public-sector](), [stig](), [visibility]() ### AI overview The article explains how the DISA Security Technical Implementation Guide for Cisco Secure Network Analytics turns Zero Trust policy into testable configuration requirements. The STIG provides a shared hardening baseline for the platform and its management functions, with 31 requirements derived from NIST SP 800-53 and related requirements. ### Source excerpt Discover how the new DISA STIG for Cisco Secure Network Analytics helps defense organizations securely configure and harden their analytics platform, ensuring trusted network visibility for Zero Trust operations. ## 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. ## Inside the LLM Call: GenAI Observability with OpenTelemetry DevFeed: [Inside the LLM Call: GenAI Observability with OpenTelemetry]() Original publisher: [Read original article]() Author: OpenTelemetry Authors; Docs CC BY Published: 2026-09-14T16:56:42Z Content type: tutorial Language: en Sources: [Blog on OpenTelemetry]() Topics: [OpenTelemetry](), [agent observability](), [telemetry](), [AI Agent](), [Large Language Model]() Tags: [agent](), [ai-agent](), [claude-code](), [codex](), [observability](), [opentelemetry](), [token](), [tool](), [visibility]() ### AI overview This tutorial explains how OpenTelemetry Semantic Conventions for Generative AI record LLM calls, tool invocations, token counts, and related events. It demonstrates exporting telemetry from an LLM-powered application, viewing it with Aspire Dashboard, and considering sensitive-data implications of optional content capture. ### Source excerpt Your AI agent just took 45 seconds to answer a simple question. Was it the model? A slow tool call? A retry loop? Every time an application calls an LLM, a chain of model calls, tool invocations, and token exchanges happens behind the scenes -- and without observability, you are guessing. The OpenTelemetry Semantic Conventions for Generative AI give you that visibility. They standardize how GenAI operations are recorded -- the model being called, input and output token counts, and when opted in, the full content of prompts, completions, tool calls, and tool results. ## How to measure and improve instrumentation quality for better full-stack observability DevFeed: [How to measure and improve instrumentation quality for better full-stack observability]() Original publisher: [Read original article]() Author: Arpit kumar Published: 2026-09-12T11:22:06.456390Z Content type: article Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [Instrumentation](), [observability](), [Grafana Cloud](), [telemetry]() Tags: [analytics](), [grafana](), [grafana-cloud](), [how-to](), [instrumentation](), [knowledge-graph](), [logs](), [metrics](), [monitoring](), [observability](), [traces]() ### AI overview This article explains how to measure and improve instrumentation quality for full-stack observability. It introduces Grafana Cloud's continuously updated instrumentation quality report, which evaluates each service using automated checks for telemetry completeness, correctness, signal coverage, naming, Kubernetes correlation, and connections across the service graph. ### Source excerpt Modern engineering teams instrument everything, with metrics, logs, traces, and profiles flowing from hundreds of services at once. But full-stack observability isn't really about collecting more telemetry; it's about having a single, unified picture of how your services connect to every layer beneath them, including their dependencies, the pods and nodes they run on, and the logs, traces, and profiles that explain their behavior. But there's often a quiet problem hiding underneath all that data: not all instrumentation is created equal, and every gap silently breaks one of those connections. One service, for example, might emit metrics but no logs, so when you pivot from "this is erroring" to "show me why," you hit a dead end. Another might have logs but an invalid service.name or a missing k8s.pod.name that breaks correlation, dropping it out of the graph and away from its pods and nodes. A third service might look perfectly healthy right up until an incident, when you discover its traces were never wired up and the trail goes cold exactly when you need it most. To fix this, Grafana Cloud's Knowledge Graph now includes an instrumentation quality report: an automated, continuously updated assessment of how well each of your services is instrumented--and, in effect, how they plug into the full-stack picture. In this post, we'll walk through how to read the instrumentation quality report, how the scoring works, and how to use it to systematically raise the observability bar, keeping every layer of your stack joined up across every service you run. What is instrumentation quality? Instrumentation quality is a measure of how complete and correct the telemetry for a given service is, judged against a set of automated checks. Each service is evaluated by a server-computed set of quality checks: small, focused rules that validate one specific thing about a service's telemetry. A few examples: Does the service emit logs? Are service graph metrics present? Is the service nam ## 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 ## 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. ## 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 ## Monitoring production agent lifecycle with AWS DevOps Agent and AgentCore Evaluations DevFeed: [Monitoring production agent lifecycle with AWS DevOps Agent and AgentCore Evaluations]() Original publisher: [Read original article]() Author: Meghana Ashok Published: 2026-09-11T18:26:38Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [ai observability](), [AWS IAM](), [incident](), [Orchestration](), [telemetry]() Tags: [advanced-300](), [agent](), [agents](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [aws](), [aws-identity-and-access-management-iam](), [best-practices](), [devops](), [incident](), [infrastructure-monitoring](), [logs](), [metrics](), [monitoring](), [orchestration](), [production](), [tracing]() ### AI overview The article describes monitoring production multi-agent systems with Amazon Bedrock AgentCore Evaluations for continuous quality assessment and AWS DevOps Agent for autonomous infrastructure incident investigation. ### Source excerpt Multi-agent systems fail in ways traditional monitoring misses. This post presents a dual-layer approach to monitoring production agents: Amazon Bedrock AgentCore Evaluations for continuous quality scoring and AWS DevOps Agent for autonomous infrastructure investigation, shown on a four-agent airline reservation system. ## How AWS Lambda logs every flow across thousands of microVMs per host with eBPF and Rust DevFeed: [How AWS Lambda logs every flow across thousands of microVMs per host with eBPF and Rust]() Original publisher: [Read original article]() Author: Prashant Kumar Singh Published: 2026-09-11T12:00:00Z Content type: article Language: en Sources: [The New Stack]() Topics: [AWS Lambda](), [Architecture & Design](), [SIEM, Security, Observability](), [telemetry](), [Amazon EC2](), [Amazon S3](), [VPC]() Tags: [architecture](), [aws](), [aws-lambda](), [aws-marketplace](), [compliance](), [ebpf](), [firecracker](), [incident](), [incident-response](), [logs](), [monitoring](), [observability](), [post-contributed](), [rust](), [s3](), [scale](), [security](), [server](), [serverless](), [sponsor-aws-marketplace](), [sponsored-post-contributed](), [vpc]() ### AI overview AWS Lambda describes replacing an aging network-capture system with an eBPF and Rust pipeline that records network flows across short-lived, tenant-isolated microVMs. The system prioritizes complete, correctly attributed records with minimal overhead for security investigation, metering, audit, observability, and monitoring. ### Source excerpt On any compute platform, when a security alert fires, the question is always the same. Which workload talked to that The post How AWS Lambda logs every flow across thousands of microVMs per host with eBPF and Rust appeared first on The New Stack. ## Understanding NetFlow duplication: Why it happens, and how to deduplicate DevFeed: [Understanding NetFlow duplication: Why it happens, and how to deduplicate]() Original publisher: [Read original article]() Author: Julie Wang; Garrison Stauffer Published: 2026-09-11T00:00:00Z Content type: tutorial Language: en Sources: [Datadog | The Monitor blog]() Topics: [telemetry]() Tags: [how-to](), [metrics](), [monitoring](), [netflow](), [network-device-monitoring](), [networks]() ### AI overview The article explains why NetFlow flow records can be duplicated and how duplicate traffic data distorts network analytics. It covers ingress/egress and multi-exporter duplication, recommends selective monitoring, and introduces deduplication in Datadog. ### Source excerpt Learn about the underlying factors that cause duplication of NetFlow traffic flow data, how to prevent the issue, and how Datadog can help. ## 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 [...] [Next page]()