# observability Observability is the ability to understand a complex system's internal state from its external telemetry, supporting the operation, troubleshooting, and security of software systems and cloud computing environments. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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... ## AI Agent Governance: Why It Belongs in Your Platform DevFeed: [AI Agent Governance: Why It Belongs in Your Platform]() Original publisher: [Read original article]() Author: Prateek Mittal Published: 2026-09-16T20:28:57.610955Z Content type: article Language: en Sources: [Harness Blog]() Topics: [AI Agent](), [Artificial Intelligence](), [observability](), [audit](), [test](), [Deployment](), [Monitoring](), [pull-requests]() Tags: [ai](), [ai-agent](), [audit](), [deployment](), [governance](), [monitoring](), [observability](), [pull-requests](), [test]() ### AI overview Engineering leaders from Yelp, Platformable, and Harness argue that AI agent governance must be automated, continuously enforced, and built into the platform rather than left to policy documents. The article discusses audit trails, agent-to-agent access controls, experiment tracking, testing, latency monitoring, rollback paths, and observability for agent-driven changes. ### Source excerpt Engineering leaders from Yelp, Platformable, and Harness explain why AI agent governance has to be built into the platform, not a policy doc. | Blog ## 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 [...] ## Presentation: Teaching Engineers, Trusting AI: How Education Enabled Autonomous Code Review DevFeed: [Presentation: Teaching Engineers, Trusting AI: How Education Enabled Autonomous Code Review]() Original publisher: [Read original article]() Author: Sarah Deitke Published: 2026-09-16T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [Artificial Intelligence](), [Code review](), [Pull Request](), [Tooling](), [engineering-culture](), [observability]() Tags: [adoption](), [agile](), [ai](), [ai-ml-data-engineering](), [architecture-design](), [artificial-intelligence](), [automated](), [automation](), [autonomous](), [case-study](), [code-review](), [code-reviews](), [culture](), [culture-methods](), [developer](), [developer-experience](), [development](), [duolingo-ai-literacy-code-review](), [infoq](), [metrics](), [ml-data-engineering](), [observability](), [pairing](), [presentation](), [qcon-london-2026](), [qcon-software-development-conference](), [transcripts]() ### AI overview Sarah Deitke presents Duolingo's approach to cultural AI adoption through internal AI literacy workshops, observability dashboards, and safe AI guardrails. The presentation includes a case study on redesigning code review with an automated PR risk-assessment bot and reports faster delivery without increased defect rates. ### Source excerpt Sarah Deitke discusses how Duolingo drives cultural AI adoption beyond tooling access. She explains their internal AI literacy workshops and observability dashboards, then shares a case study on redesigning code review using an automated PR risk-assessment bot. Deitke demonstrates how pairing targeted developer education with safe AI guardrails speeds up delivery without increasing defect rates. By Sarah Deitke ## Elastic announces GA of cross-project search on Serverless, enabling teams to query across all linked projects without moving a byte DevFeed: [Elastic announces GA of cross-project search on Serverless, enabling teams to query across all linked projects without moving a byte]() Original publisher: [Read original article]() Author: Jordi Mon Companys,Lucy Wang Published: 2026-09-16T00:00:00Z Content type: release Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [Serverless](), [Elastic Cloud](), [Cloud](), [observability](), [Security](), [SIEM, Security, Observability]() Tags: [architecture-cloud-migration-migrating-cloud-native-scaling-search-analytics-search-applicatio](), [cloud](), [cloud-elasticsearch-platform](), [customers](), [elastic](), [elastic-cloud](), [observability](), [observability-security-search](), [security](), [serverless]() ### AI overview Elastic announced the general availability of cross-project search for Elastic Cloud Serverless. It lets teams query multiple linked projects in a single session across regions, project types, and cloud providers, while keeping data in place and avoiding certificates, remote-cluster configuration, and per-connection authentication. ### Source excerpt Cross-project search is now GA on Elastic Cloud Serverless. Link multiple projects and query all of them from a single Discover or ES|QL session: no certificates, no remote cluster config, and three clicks to set up. Available from September 1, 2026. ## How end-to-end SLO monitoring detected a livestream failure that component dashboards missed DevFeed: [How end-to-end SLO monitoring detected a livestream failure that component dashboards missed]() Original publisher: [Read original article]() Author: Whatnot Engineering Published: 2026-09-15T16:31:01Z Content type: article Language: en Sources: [Whatnot Engineering]() Topics: [dashboards](), [observability](), [incident](), [Monitoring](), [Web](), [client](), [Network]() Tags: [dashboards](), [distributed-systems](), [incident](), [monitoring](), [observability](), [security](), [server](), [site-reliability-engineer](), [software-engineering](), [web]() ### AI overview The article examines a June 8, 2026 incident in which a third-party client-side security script fetched from a provider CDN added URL validation that the video provider did not pass, causing black screens for newly loaded web clients. Most component-level dashboards remained green, while end-to-end service-level objective monitoring detected the broken livestream experience and paged the owning teams within five minutes. ### Source excerpt Karol Gil | Reliability Platform (Poland) On June 8, 2026, newly loaded web clients began showing black screens instead of livestream video. For our platform, that's a serious problem: it's pretty hard to sell Pokémon cards that no one can see. It turned out that a third-party script we use for client-side security monitoring wasn't bundled with our release, but was rather fetched live from the provider's CDN. When the provider updated the script all new web clients fetched it, and it included an additional URL validation which our video provider didn't pass. The result? Black screens for users of the affected web clients, with most internal dashboards staying green. 3,000 users were impacted in the first 30 minutes of the incident. One system did catch it. Our end-to-end service-level objective (E2E SLO) monitoring was already in production and paged the owning teams within five minutes. Here's what it saw. The real problem Most of our dashboards stayed green because they monitor component-level health: a server, an endpoint, a specific function. These are all useful, but can all be healthy while the actual user experience is completely broken. This problem gets worse the more external dependencies there are, or the more sophisticated an experience you want to deliver. In complex, integrated product experiences like ours, a "small" problem can have an outsize impact on the user experience. Measuring this requires a different approach to observability, namely, to model the user journey across multiple surfaces that must be true for a customer to have a good experience. So how do we measure this in a complex distributed application? Joining a livestream is not one thing Joining a livestream sounds like one action, but the user expects at least three things: Video to be playing Auction details to be shown Chat to be visible and up to da Each of those can succeed or fail completely independently of the other two. Our video depends on third-party providers and CDN netwo ## 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. ## QEMU Google Summer of Code 2026 project report DevFeed: [QEMU Google Summer of Code 2026 project report]() Original publisher: [Read original article]() Published: 2026-09-15T07:00:00Z Content type: article Language: en Sources: [QEMU]() Topics: [qemu](), [AdventureX 2025](), [Open Source](), [virtual machines](), [x86](), [observability](), [Rust]() Tags: [cpu](), [development](), [google-summer-of-code](), [gsoc](), [internships](), [observability](), [open-source](), [qemu](), [rust]() ### AI overview QEMU's Google Summer of Code 2026 report covers four contributors' open-source projects from May through August, including lazy snapshot loading, x86 Process Context Identifier support in COCONUT-SVSM, and observability support for confidential virtual machines. ### Source excerpt QEMU participated in Google Summer of Code 2026 with 4 contributors working on open source internships from May through August. The contributors gained experience in open source software development working on 12-week projects. ## How Solaris' Turnstile Influenced the Modern System Designs of Web Browsers and Language Runtimes DevFeed: [How Solaris' Turnstile Influenced the Modern System Designs of Web Browsers and Language Runtimes]() Original publisher: [Read original article]() Author: Olimpiu Pop Published: 2026-09-15T06:06:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [systems](), [Programming](), [Kernel](), [observability](), [browsers](), [web browsers]() Tags: [architecture](), [architecture-design](), [architectures](), [blocking](), [concurrency](), [development](), [efficiency](), [kernel](), [locking](), [memory](), [news](), [observability](), [operating-systems](), [solaris](), [storage](), [systems](), [turnstile-system-design]() ### AI overview The article examines how Solaris innovations influenced modern systems engineering. It discusses the Slab Allocator, OpenZFS, DTrace, Zones, and turnstiles, focusing on how turnstiles reduce per-lock memory overhead while supporting priority inheritance for contended locks. ### Source excerpt Sun's Solaris influenced modern systems engineering, with key innovations like the Slab Allocator for efficient memory management, OpenZFS for advanced storage, and DTrace for observability. Its turnstile mechanism addressed issues with mutexes, promoting lean locking designs that are reflected in contemporary programming languages and web browsers, enhancing performance and memory efficiency. By Olimpiu Pop ## OpenSearch Wins Analytics & Data Intelligence Solutions Category in the SiliconANGLE TechForward Awards DevFeed: [OpenSearch Wins Analytics & Data Intelligence Solutions Category in the SiliconANGLE TechForward Awards]() Original publisher: [Read original article]() Author: Kristi Piechnik Published: 2026-09-14T12:00:14Z Content type: news Language: en Sources: [OpenSearch]() Topics: [Open Source](), [AI Infrastructure](), [observability](), [AI search](), [Security](), [Retrieval Augmented Generation (RAG)](), [Databases](), [real-time]() Tags: [2026](), [agentic-ai](), [ai-agents](), [ai-infrastructure](), [announcements](), [awards](), [data](), [enterprise](), [observability](), [open-source](), [opensearch](), [real-time](), [recognition](), [retrieval](), [retrieval-augmented-generation-rag](), [search](), [security]() ### AI overview OpenSearch won the Analytics & Data Intelligence Solutions category in SiliconANGLE Media's 2026 TechForward Awards. The recognition highlights its open source, vendor-neutral platform for enterprise search, observability, security analytics, vector databases, and agentic AI workloads. ### Source excerpt Recognition validates open source momentum, architectural consolidation, and enterprise scale as the project marks five years of community growth The post OpenSearch Wins Analytics & Data Intelligence Solutions Category in the SiliconANGLE TechForward Awards appeared first on OpenSearch. ## Datadog named the Company to Beat for observability platforms in 2026 Gartner® AI Vendor Race report DevFeed: [Datadog named the Company to Beat for observability platforms in 2026 Gartner® AI Vendor Race report]() Original publisher: [Read original article]() Author: Yanbing Li Published: 2026-09-14T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [observability](), [agent observability](), [observability ai agents](), [incident](), [MCP Server](), [observability pipelines](), [OpenTelemetry](), [Security](), [Artificial Intelligence]() Tags: [agent-observability](), [ai-agents](), [datadog](), [incident-response](), [mcp-server](), [observability](), [observability-pipelines](), [opentelemetry]() ### AI overview Datadog says it was named the Company to Beat for observability platforms in Gartner's August 2026 AI Vendor Race research and a Leader in the 2026 Gartner Magic Quadrant for Observability Platforms. The article presents Datadog's unified observability and security platform, including autonomous incident investigation, AI agent and LLM application observability, an MCP Server for querying telemetry, and Observability Pipelines with OpenTelemetry support. ### Source excerpt Datadog has been recognized as the Company to Beat for observability platforms in the August 2026 Gartner® AI Vendor Race research. ## It passed CI. It passed your evals. The customer still got the wrong answer. DevFeed: [It passed CI. It passed your evals. The customer still got the wrong answer.]() Original publisher: [Read original article]() Author: Sean O'Dell Published: 2026-09-13T14:00:00Z Content type: article Language: en Sources: [The New Stack]() Topics: [AI Agent](), [agent observability](), [observability](), [tracing](), [dynatrace](), [ci](), [coding]() Tags: [ai](), [ai-agent](), [ai-agents](), [ai-engineering](), [ci](), [coding](), [dynatrace](), [observability](), [post-contributed](), [sponsor-dynatrace](), [sponsored](), [sponsored-post-contributed](), [traces]() ### AI overview The article explains how AI-agent failures can pass CI and evaluations while still producing slow or incorrect customer-facing results. It presents distributed traces and agent trajectories--model calls, tool calls, arguments, and results--as evidence for debugging retrieval behavior, release context, and feature-flag state. ### Source excerpt A diff is not evidence. It's a statement of intent. The tests passed. The review's done. The change is live. The post It passed CI. It passed your evals. The customer still got the wrong answer. appeared first on The New Stack. ## From failed check to real user impact: Pairing Synthetic Monitoring and Frontend Observability in Grafana Cloud DevFeed: [From failed check to real user impact: Pairing Synthetic Monitoring and Frontend Observability in Grafana Cloud]() Original publisher: [Read original article]() Author: Mark Meier Published: 2026-09-12T11:22:06.456390Z Content type: tutorial Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [synthetic monitoring](), [Frontend observability](), [Grafana Cloud](), [observability]() Tags: [analytics](), [frontend-observability](), [grafana](), [grafana-cloud](), [logs](), [metrics](), [monitoring](), [observability](), [synthetic-monitoring](), [traces](), [uptime]() ### AI overview This article explains how to combine Grafana Cloud Synthetic Monitoring with Frontend Observability to connect proactive checks with real-user impact. It describes the blind spots of synthetic-only monitoring and presents a combined workflow for faster triage, impact-aware alerts, and tests that evolve with real traffic. ### Source excerpt Say you get a support escalation about a page in the app that won't load. But when you pull up your synthetic checks, they're all green: 100% uptime, probes are passing. Something's not adding up, but which one do you trust? If you've run Grafana Cloud Synthetic Monitoring, you've been on both sides of this. Sometimes it's the ticket: real users hit a wall on the path but your checks pass cleanly. Other times, it's the inverse: a check is failing, you're in a panic, and you start trying to reproduce things for 30 minutes--only to find it was a blip from a single region, with minimal impact to real users. Neither the green dashboard nor the red alert were lying, they just weren't answering the correct question. This ends up being the root problem. Synthetic Monitoring is exceptionally good at telling you if something broke. It can not, however, tell you who it happened to, how bad it was, or why it matters. This is not a flaw in Synthetic Monitoring; it's the boundary of what a controlled, scheduled test can know. Grafana Cloud Frontend Observability helps to close this gap. Synthetic Monitoring gives you a proactive, outside-in signal; Frontend Observability gives you the real-user, inside-out signal. Together they form a closed loop: synthetic alerts end up getting some real user context, and real user data can make your synthetic tests smart. In this post, we'll look at why a synthetic-only strategy can leave blind spots, what Frontend Observability adds, and walk through practical workflows for running them together in Grafana Cloud. Along the way, you'll learn that the payoff is concrete: faster triage, alerts that carry blast-radius context, and a check suite that evolves with real traffic instead of aging against it. Green checks don't mean happy users Synthetic Monitoring is an active signal. You script a journey or declare a target, run it on a schedule from known probe locations, and in return get clean consistent results. This precise control of variables i ## 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 ## Wrapture: a Python package for monkey patching, testing, and observability DevFeed: [Wrapture: a Python package for monkey patching, testing, and observability]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-11T13:51:32Z Content type: opinion Language: en Sources: [Simon Willison's Weblog]() Topics: [Python](), [Testing](), [observability](), [Instrumentation](), [tracing](), [OpenTelemetry](), [Flask](), [jupyterlab]() Tags: [code](), [graham-dumpleton](), [graham-dumpleton-6](), [how-to](), [instrumentation](), [monkey-patching](), [monkey-patching-10](), [observability](), [observability-10](), [open-source](), [open-source-320](), [opentelemetry](), [python](), [python-1-283](), [testing](), [testing-95](), [tracing]() ### AI overview This article reviews wrapture, an alpha Python package by Graham Dumpleton for monkey patching, unit testing, call recording, live and zero-code tracing, timing analysis, and OpenTelemetry export. It highlights tutorials and JupyterLab workshops covering the package and related instrumentation. ### Source excerpt Graham Dumpleton's new monkey patching package wrapture is shaping up to be an indispensable tool for Python developers. I'm not sure why I've seen so little buzz about it! Graham has been posting new tutorials for it almost daily since the initial release on August 31st. Here's everything he's published so far: Introducing wrapture - a new monkey patching library that serves both testing and observability (think New Relic style tracing) at the same time. Unit testing with wrapture - how to use it for the same kinds of thing as unittest.mock. Recording calls with wrapture - recording method calls as timelines and processing and displaying them as trees. Phased behaviour in wrapture - arranging patched methods to change behavior across multiple calls. Beyond callables in wrapture - monkey patching attributes, dictionaries, generators. Live tracing with wrapture - tracing a live application to see exactly how it works. Zero-code tracing with wrapture - configuring tracing in a separate TOML file without modifying Python code at all. Tracing Flask with wrapture - using the separate wrapture-instrumenation package to instrument a Flask application. That package also provides instrumentation for aiohttp.client, aiohttp.web, django, fastapi, flask, grpc, http.client, httpx, jinja2, requests, sqlalchemy, sqlite3, starlette, urllib.request, urllib3, uvicorn, werkzeug.serving, wsgiref.simple_server, xmlrpc.client, xmlrpc.server. Finding slow code with wrapture - wrapture's tools for recording timing information, both individually and aggregated across multiple calls. OpenTelemetry export in wrapture - exporting traces to OpenTelemetry. Graham also has a set of interactive workshops for wrapture, implemented as JupyterLab notebooks. Wrapture is still alpha software but it's already very usable - especially given you can configure and try it out with a TOML file without modifying any Python code at all. This feels like one of those Swiss Army Knife packages that, once mastered ## Beyond the 200 OK: Architecting Observability for AI DevFeed: [Beyond the 200 OK: Architecting Observability for AI]() Original publisher: [Read original article]() Author: Adriano Mota Published: 2026-09-11T07:00:00Z Content type: article Language: en Sources: [Nordic APIs]() Topics: [ai observability](), [observability](), [Application Performance Management (APM)](), [AI Agent](), [Artificial Intelligence]() Tags: [ai](), [ai-agent](), [ai-agents](), [ai-observability](), [api-logging](), [api-metrics](), [api-monitoring](), [api-security](), [api-testing](), [apm](), [artificial-intelligence](), [blog](), [generative-ai](), [llm](), [mcp](), [observability]() ### AI overview An article about designing observability for AI systems beyond traditional APM metrics, with emphasis on tracking quality, cost, retrieval, and agent behavior. ### Source excerpt Traditional monitoring tools, such as application performance monitoring (APM), were engineered to monitor deterministic software where specific inputs reliably lead to predictable outputs through hard-coded logic. When a traditional API fails, it usually throws a 500 Internal Server Error. But when an AI agent fails, it might return a perfectly healthy 200 OK status code ... ## Agent Gateway: The Next Evolution of the API Gateway DevFeed: [Agent Gateway: The Next Evolution of the API Gateway]() Original publisher: [Read original article]() Author: Gbadebo Bello Published: 2026-09-10T16:00:00Z Content type: article Language: en Sources: [Postman Blog]() Topics: [API](), [AI Agent](), [Architecture & Design](), [Artificial Intelligence](), [Routing (disambiguation)](), [Authentication](), [Authorization](), [observability]() Tags: [agent-gateway](), [ai](), [ai-agents](), [api](), [api-101](), [api-gateway](), [architecture](), [authorization](), [editor-s-picks](), [fabric-gateway](), [general](), [llm-gateway](), [mcp](), [mcp-gateway](), [observability](), [routing]() ### AI overview The article explains the emergence of the Agent Gateway as an architectural component for AI agents. It contrasts traditional API Gateways, which authenticate, govern, route, and deliver requests from clients that already know which API to call, with the needs of AI agents, which receive goals and may require broader governance across agentic workflows. ### Source excerpt API gateways are evolving for AI agents. Learn how the agent gateway governs identity, tools, memory, and policy across agentic workflows. Get early access. The post Agent Gateway: The Next Evolution of the API Gateway appeared first on Postman Blog. ## Coherence, Connections, and... Spacetime Crystals DevFeed: [Coherence, Connections, and... Spacetime Crystals]() Original publisher: [Read original article]() Author: Art Anthony Published: 2026-09-09T07:00:00Z Content type: article Language: en Sources: [Nordic APIs]() Topics: [API](), [observability](), [Statistics]() Tags: [2026](), [ai-agents](), [api](), [api-architecture](), [api-ecosystem](), [api-governance](), [api-management](), [api-platform](), [api-strategy](), [artificial-intelligence](), [blog](), [governance](), [observability](), [statistics](), [uptime]() ### AI overview The article discusses API coherence and how API estates can align with organizational goals and change. It covers business gardening, observability, governance, uptime, and usage statistics in the context of API performance and organizational intent. ### Source excerpt Ahead of his Nordic APIs Summit 2026 talk on API coherence, London Stock Exchange Group's Gareth Faull joins us to talk about the art of aligning APIs with organizational intent. Measuring the performance of an API is a relatively straightforward process: ensure observability is in place, follow governance best practices, measure uptime, track usage statistics, ... ## From AI Code to Trusted Software: Harness Engineering in Practice DevFeed: [From AI Code to Trusted Software: Harness Engineering in Practice]() Original publisher: [Read original article]() Author: Travis Frisinger Published: 2026-09-04T21:55:00Z Content type: opinion Language: en Sources: [8th Light](), [8th Light Insights]() Topics: [AI-assisted coding](), [Software](), [trust](), [observability](), [Authorization]() Tags: [agent](), [ai](), [ai-and-emerging-tech](), [code](), [engineering](), [engineering-and-devops](), [observability](), [permissions](), [trust](), [verification]() ### AI overview The article presents harness engineering as a repository-centered discipline for making AI-generated software more trustworthy. It argues that prompts and written standards are insufficient, and that permissions, quality gates, evidence, and observability should enforce organizational standards and support verification. ### Source excerpt If the same reasoning path writes the system change and defines the proof of success, you may be setting yourself up for an avoidable failure in the future. Harness engineering is meant to act as an extension of your own organizational guardrails, which were always meant to reduce risk and improve quality. Travis Frisinger, Head of Agentic AI Your team is shipping more AI-written code every quarter. How do you know it is any good? Good means it meets your standards, and you have probably already tried handing your agents the standards: a context file, a style guide, the wiki pasted into the prompt. The agent reads them, agrees, and still breaks them, because instructions to a model are suggestions. What the repository permits is what actually happens. Your people absorb standards through review comments and hallway corrections, and the lessons stick. An agent apologizes and forgets by the next session. The only place its lessons can accumulate is the repository itself. That gap used to be an annoyance. With AI doing real engineering work, the quality gap is the whole game. Harness engineering is the discipline that closes it. It is the process of imbuing a repository with your standards so that the repository itself enforces them: permissions and boundaries that say what any actor may touch, quality gates that fail closed, evidence attached to every change, and observability that spans runs rather than moments. Models supply software delivery capacity. The harness supplies observable accountability: every change carries what was done, which rule allowed it, and what happened as a result, no matter which model, agent, or person did the work. Why now The industry started using the term harness engineering back in February, 2026. Since then, Thoughtworks, LangChain, and others have built serious thought leadership around the same shape. When several firms independently converge on the same word, it usually means they are trying to name the same problem. The real proble ## What 143 platform teams told us about keeping infrastructure alive DevFeed: [What 143 platform teams told us about keeping infrastructure alive]() Original publisher: [Read original article]() Author: Oliver Thylmann Published: 2026-09-04T10:15:45Z Content type: opinion Language: en Sources: [Giant Swarm Blog]() Topics: [Platform Engineering](), [Kubernetes](), [cloud-infrastructure](), [Security](), [observability]() Tags: [aws](), [azure](), [bare-metal](), [infrastructure](), [kubernetes](), [observability](), [open-source](), [platform](), [platform-engineering](), [security](), [survey](), [team](), [tech]() ### AI overview Giant Swarm surveyed 143 platform and infrastructure professionals about the ongoing work required to keep production platforms running. The article argues that this platform integration tax is primarily a time cost, with security, reliability, cluster management, and observability consuming engineering capacity. ### Source excerpt The hidden cost of platform engineering isn't building your stack. It's keeping it alive. Giant Swarm surveyed 143 platform teams to find out. ## Self-Hosted Platform Build Order: Dependencies from Bare Metal to Model Serving DevFeed: [Self-Hosted Platform Build Order: Dependencies from Bare Metal to Model Serving]() Original publisher: [Read original article]() Author: Philipp Lehmann (philipp.lehmann@gruppe.ai) Published: 2026-09-04T07:00:00Z Content type: article Language: en Sources: [Philipp Lehmann]() Topics: [Self-hosted](), [Ansible](), [Kubernetes](), [ceph](), [observability](), [model-serving](), [Compose]() Tags: [ansible](), [architecture](), [article](), [ceph](), [compose](), [infrastructure-as-code](), [kubernetes](), [model-serving](), [observability](), [self-hosted]() ### AI overview This article explains the dependency order for building a self-hosted platform. It covers consistent bare-metal inventory, Ansible configuration convergence, networking, Kubernetes, Ceph storage, identity, observability, and model serving. ### Source excerpt Self-hosted platform build order: why mesh, cluster, Ceph storage, identity and observability must precede model serving, shown with Compose depends_on. ## Week Ending August 30, 2026 DevFeed: [Week Ending August 30, 2026]() Original publisher: [Read original article]() Published: 2026-09-04T01:00:00Z Content type: news Language: en Sources: [Last Week in Kubernetes Development]() Topics: [Kubernetes](), [Open Source](), [observability](), [Cloud](), [Kubernetes v1.32]() Tags: [2026](), [cloud-native](), [developer](), [kubernetes](), [lifecycle](), [news](), [observability](), [open-source](), [release]() ### AI overview A weekly Kubernetes developer news roundup covering the Maintainer Summit schedule, the KubeCon Europe CfP, the 2026 Kubernetes Steering Committee election, KCD San Francisco Bay Area, the Kubernetes v1.38 release cycle, and recent StatefulSet observability and Dynamic Resource Allocation fixes. ### Source excerpt Developer News ## New AI and Kubernetes Private Cloud Operations Capabilities in VMware Cloud Foundation 9.1.1 DevFeed: [New AI and Kubernetes Private Cloud Operations Capabilities in VMware Cloud Foundation 9.1.1]() Original publisher: [Read original article]() Author: vmwareblogs Published: 2026-09-03T14:00:05Z Content type: release Language: en Sources: [VMware Blogs]() Topics: [vcf 9.1](), [vcf operations](), [Artificial Intelligence](), [Conversational AI](), [Kubernetes](), [private cloud](), [observability](), [Security]() Tags: [ai](), [cloud-infrastructure](), [conversational-ai](), [costoptimization](), [diagnostics](), [kubernetes](), [operations](), [private-cloud](), [release](), [security](), [vcf-9-1](), [vcf-management-services](), [vcf-operations](), [vcf-operations-for-logs](), [vcf-operations-for-networks](), [visibility](), [vks](), [vmware-cloud-foundation]() ### AI overview VMware announces new operations capabilities in VMware Cloud Foundation 9.1.1, including expanded visibility, an optional locally configured conversational AI assistant, full-stack Kubernetes observability, and enhanced security operations. ### Source excerpt We are excited to announce new operations capabilities with the release of VMware Cloud Foundation (VCF) 9.1.1. By expanding visibility capabilities, giving you the option to configure a conversational AI assistant to assist your daily workflows, and hardening security, VCF 9.1.1 helps IT teams resolve issues faster and scale their infrastructure. As private cloud infrastructure ... Continued The post New AI and Kubernetes Private Cloud Operations Capabilities in VMware Cloud Foundation 9.1.1 appeared first on VMware Blogs. [Next page]()