# SRE Site Reliability Engineering (SRE) is an engineering discipline for sustainably achieving appropriate reliability in software systems, services, and products. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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. ## Contributing to OpenSRE and Integrating It with Yandex Cloud DevFeed: [Contributing to OpenSRE and Integrating It with Yandex Cloud]() Original publisher: [Read original article]() Author: nowhere\_in\_space (Яндекс, Yandex Cloud & Yandex Infrastructure) Published: 2026-09-10T07:00:10Z Content type: article Language: ru Sources: [Яндекс - Как мы делаем Яндекс / Статьи]() Topics: [SRE](), [Cloud](), [GitHub]() Tags: [ai-e2239b5ae8fa](), [cloud](), [kubernetes](), [llm](), [opensre](), [postgresql](), [sre](), [tag-831b63de9433](), [tag-e1321f9c36de]() ### AI overview The author describes contributing to OpenSRE, an early public-alpha tool for AI SRE agents that investigate and resolve production incidents, and pursuing an integration with Yandex Cloud. The article cautions that OpenSRE's safety is not guaranteed and recommends running it without modifying permissions. ### Source excerpt Представьте: ночной алерт, приложение отдаёт пятисотки, но само оно живо. Понятно, что дальше начнётся знакомое -- вкладки с графиками, поиски в логах и попытки понять, кто и что изменил. Сколько на это обычно уходит времени? А что, если вместе с вами в инциденте будет разбираться ИИ-агент? Привет! Я Антон Воронцов, CRE в Yandex Cloud. В конце июля я в очередной раз листал свежие репозитории на GitHub, чтобы посмотреть, что происходит в моих смежных дисциплинах: SRE, автоматизациях и, понятное дело, ИИ. Вдруг я наткнулся на один интересный репозиторий, который активно рос: количество звёзд, коммиты и заинтересованные люди из разных стран -- всё это про OpenSRE. Эта статья о том, как я впервые контрибьютил во внешний проект, что стало самым сложным и как вообще работает этот инструмент. Читать далее ## 【kube-apiserver】APF 与 max-in-flight：公平排队、504 与 etcd lag 分列 DevFeed: [【kube-apiserver】APF 与 max-in-flight：公平排队、504 与 etcd lag 分列]() Original publisher: [Read original article]() Author: Liao Tonglang Published: 2026-08-28T00:00:00Z Content type: article Language: zh Sources: [土法炼钢 - 系统与基础设施]() Topics: [Kubernetes](), [API](), [SRE](), [Linux]() Tags: [apf](), [api](), [apiserver](), [distributed](), [etcd](), [fairness](), [fairqueuing](), [flowcontrol](), [k8s](), [kubernetes](), [lag](), [max-in-flight](), [priority](), [timeout](), [v1-30-3]() ### AI overview This article explains how Kubernetes v1.30.3 protects kube-apiserver from overload through API Priority and Fairness (APF) and the older max-in-flight limits. It distinguishes 429 responses, APF queue timeouts that can produce 504 responses before storage is reached, etcd latency, and admission webhook delays, and describes APF's FlowSchema, PriorityLevelConfiguration, fair queuing, and shuffle sharding mechanisms. ### Source excerpt 钉 K8s v1.30.3 的 API Priority and Fairness（APF）：FlowSchema 匹配、PriorityLevelConfiguration 公平排队（SFVR）、与旧 max-in-flight flag 的共存关系；429/timeout/504 在 APF 排队、etcd_request_duration_seconds、Admission Webhook 三轴的分列；pkg/util/flowcontrol 路径；APF 与简单 max-in-flight 的运维复杂度争论。 ## Focus and followthrough are the moat DevFeed: [Focus and followthrough are the moat]() Original publisher: [Read original article]() Author: hi@swizec.com (Swizec Teller) Published: 2026-08-27T00:00:00Z Content type: opinion Language: en Sources: [Swizec Teller]() Topics: [Development](), [SRE]() Tags: [development](), [focus](), [production](), [projects](), [sre](), [team]() ### AI overview An opinion piece about how a small startup team uses focus, sprint planning, prioritization, and regular stakeholder communication to finish work despite having more requests than capacity. ### Source excerpt Because starting is easy and finishing is hard. ## Centralize human and agentic work with Datadog Work Management DevFeed: [Centralize human and agentic work with Datadog Work Management]() Original publisher: [Read original article]() Author: Roxanne Moslehi Published: 2026-08-18T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [AI Agent](), [incident](), [site-reliability-engineering](), [Slack](), [SRE](), [SIEM, Security](), [GitHub](), [error tracking](), [dashboards](), [Traces]() Tags: [agentic](), [ai](), [ai-agent](), [cloud-siem](), [dashboards](), [devops](), [devsecops](), [error-tracking](), [github](), [incident](), [management](), [slack](), [sre](), [traces](), [work-management](), [workflow-automation]() ### AI overview Datadog Work Management centralizes work created by people, automations, and Datadog AI agents. It preserves context from logs, traces, monitors, alerts, ownership, assignments, approvals, artifacts, and activity while integrating with Datadog and external collaboration systems. ### Source excerpt Learn how Datadog Work Management helps you coordinate human and AI agent-driven work while preserving context, ownership, and activity across tools. ## Automate Incident Intake with AI SRE Runbooks DevFeed: [Automate Incident Intake with AI SRE Runbooks]() Original publisher: [Read original article]() Author: Ryan Taylor Published: 2026-08-10T00:00:00Z Content type: tutorial Language: en Sources: [Harness Blog]() Topics: [SRE](), [incident](), [Automation](), [Slack](), [zoom](), [Grafana](), [datadog](), [Artificial Intelligence]() Tags: [ai](), [automation](), [build](), [datadog](), [grafana](), [harness](), [incident](), [integrations](), [jira](), [open](), [services](), [slack](), [sre](), [workflow](), [zoom]() ### AI overview This article explains how Harness AI SRE runbooks automate incident intake and early response. Triggered by alerts, manual actions, or incident changes, a runbook can create tickets, open Slack channels, start Zoom bridges, set incident fields, and record actions in the incident timeline. ### Source excerpt Automate incident intake with Harness AI SRE runbooks: auto-create tickets, open Slack channels, start Zoom bridges, and cut response time to seconds. | Blog ## What your AI SRE can't see (and what you can do about it) DevFeed: [What your AI SRE can't see (and what you can do about it)]() Original publisher: [Read original article]() Author: Ryan Detwiller Published: 2026-08-06T00:00:00Z Content type: opinion Language: en Sources: [Gremlin Blog]() Topics: [Artificial Intelligence](), [SRE](), [incident](), [Availability](), [telemetry](), [Latency](), [Memory Leaks](), [Deployment]() Tags: [ai](), [availability](), [config](), [dependency](), [deployment](), [latency](), [load-balancer](), [memory](), [outage](), [outages](), [sre]() ### AI overview The article argues that AI SRE tools can speed up triage, reduce alert fatigue, and automate frontline incident response, but they do not solve all reliability problems. It identifies gaps including acting only after failures begin and being unable to predict sudden failures without detectable warning signals. ### Source excerpt AI SRE is having a moment. And let's be honest: faster triage, less alert fatigue, and automated frontline response are wins for understaffed teams. But there are still five gaps in their capabilities, and if you don't understand those gaps before you deploy, you'll find out during an outage. ## Cloud provider postmortems: volume vs depth DevFeed: [Cloud provider postmortems: volume vs depth]() Original publisher: [Read original article]() Author: Sridhar Rajarao Published: 2026-08-05T00:00:00Z Content type: opinion Language: en Sources: [Sridhar Rajarao]() Topics: [Cloud](), [incident](), [SRE](), [site-reliability-engineering](), [engineering-culture]() Tags: [2017](), [2025](), [2026](), [aws](), [azure](), [cloud](), [dynamodb](), [engineering-culture](), [gcp](), [incident](), [incident-management](), [postmortems](), [s3](), [sre](), [transparency](), [writeup]() ### AI overview The article compares public postmortem practices among Google Cloud, Azure, and AWS. It argues that Google Cloud emphasizes high volume and speed, Azure emphasizes detailed transparency and customer accountability, and AWS publishes fewer writeups with greater depth and industry influence. ### Source excerpt GCP publishes 100+ postmortems a year. AWS publishes almost none. Azure has become the transparency leader. What each posture reveals about engineering culture, and what SREs should steal from all three. ## You need reliable AI context for your site reliability DevFeed: [You need reliable AI context for your site reliability]() Original publisher: [Read original article]() Author: Phoebe Sajor Published: 2026-07-28T07:40:00Z Content type: article Language: en Sources: [Stack Overflow Blog]() Topics: [site-reliability-engineering](), [SRE](), [AI Agent](), [Artificial Intelligence](), [Kubernetes](), [AI Platform]() Tags: [agent](), [ai](), [ai-agent](), [ai-engineering](), [autonomous](), [cloud](), [engineering](), [kubernetes](), [platform](), [podcast](), [se-stackoverflow](), [se-tech](), [site-reliability-engineering](), [sre](), [strategy]() ### AI overview A discussion of reliable AI context for modern site-reliability work, including context engineering, Kubernetes-based infrastructure, and the changing role of human SREs in strategy and AI agent management. ### Source excerpt Ryan is joined by Asaf Savich, Komodor's AI Engineering Group Manager, to discuss why modern reliability work requires navigating massive cross-service context, what good context engineering actually likes when AI is integrated into site reliability, and how the work of human SREs is shifting towards strategy and AI agent management. ## ITIL vs SRE: why the big clouds went their own way DevFeed: [ITIL vs SRE: why the big clouds went their own way]() Original publisher: [Read original article]() Author: Sridhar Rajarao Published: 2026-07-26T00:00:00Z Content type: opinion Language: en Sources: [Sridhar Rajarao]() Topics: [SRE](), [site-reliability-engineering](), [Development](), [Terraform](), [pulumi](), [Availability](), [feature flags](), [incident]() Tags: [automated](), [aws](), [azure](), [cloud](), [feature-flags](), [gcp](), [human-review](), [hyperscaler](), [incident](), [incident-management](), [infrastructure-as-code](), [itil](), [on-call](), [postmortems](), [pulumi](), [release](), [reliability](), [service-catalog](), [sre](), [terraform]() ### AI overview This opinion article compares ITIL practices with SRE operations at hyperscaler scale. It argues that human change boards, single production instances, developer-to-operations handoffs, documentation-first configuration management, and weekly release windows do not fit environments serving millions of external customers. It describes automated approvals, gradual deployments, service-team ownership, infrastructure as code, continuous release, error budgets, SLOs, and blameless postmortems as alternatives. ### Source excerpt The big clouds don't run ITIL. Five assumptions ITIL makes that break at hyperscaler scale, and what AWS, Azure, GCP, and OCI use instead. ## Control Reliability Engineering (CRE): Applying SRE Principles to Cybersecurity Controls DevFeed: [Control Reliability Engineering (CRE): Applying SRE Principles to Cybersecurity Controls]() Original publisher: [Read original article]() Author: phil7672 Published: 2026-07-25T05:52:21Z Content type: article Language: en Sources: [Risk and Cyber]() Topics: [Cybersecurity](), [SRE](), [Monitoring](), [reliability](), [plotting]() Tags: [benchmarking](), [cybersecurity](), [leadership](), [monitoring](), [reliability](), [reliability-engineering](), [risk](), [sre](), [technology]() ### AI overview This article applies SRE principles to cybersecurity controls, arguing that control effectiveness matters more than input-focused budget comparisons. It highlights continuous control monitoring to detect controls that are broken, misconfigured, or incomplete when needed. ### Source excerpt Security breaches are often not the result of awesome attacker capabilities or the sudden emergence of sophisticated zero-day exploits. Instead, what we usually find are the controls designed to stop the attack were believed to be operational but were actually broken or misconfigured at the moment when they were needed. Sometimes they were never fully in place to meet the security team's original intent. So, continuous control monitoring is needed to counter the natural decay that occurs to... ## Why platform engineering will eat the world DevFeed: [Why platform engineering will eat the world]() Original publisher: [Read original article]() Author: Kaspar von Grünberg Published: 2026-07-23T08:08:12Z Content type: opinion Language: en Sources: [Platform Engineering Blog]() Topics: [Platform Engineering](), [internal developer platform](), [Developer Platform](), [software-development](), [Software Engineering](), [Developer experience](), [site-reliability-engineering](), [SRE](), [observability](), [Security]() Tags: [developer-experience](), [developer-platform](), [development](), [engineering](), [internal-developer-platform](), [operations](), [platform](), [platform-engineering](), [security](), [software](), [software-development](), [sre]() ### AI overview The article argues that platform engineering is becoming the dominant model for large-scale software development. It describes platforms as structured digital production lines that reduce time to market, consolidate functions such as operations, databases, security, observability, and SRE, and support greater speed, scale, and security. ### Source excerpt Platform engineering is transforming software development, streamlining operations, and redefining roles. Embrace the shift or risk becoming obsolete. ## From DevOps to Platform Engineer: Navigating your career journey DevFeed: [From DevOps to Platform Engineer: Navigating your career journey]() Original publisher: [Read original article]() Author: Sarah Kruger Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Platform Engineering](), [Developer experience](), [DevOps](), [internal developer portal](), [SRE](), [Infrastructure as code](), [Orchestration]() Tags: [automation](), [cloud-architecture](), [configuration](), [developer-experience](), [devops](), [infrastructure](), [infrastructure-as-code](), [kubernetes](), [platform](), [platform-engineering]() ### AI overview This career guide explains how DevOps and SRE professionals can transition into platform engineering. It emphasizes moving from direct operational execution to building internal platforms, golden paths, and self-service workflows that help developers deliver software safely and efficiently. The guide also highlights product thinking, developer research, developer portals, policy-driven configuration, and measuring developer experience and business impact. ### Source excerpt Transitioning from DevOps to Platform Engineering? This guide maps the essential technical and product skills you need, from mastering golden paths and developer portals to adopting product thinking and user research. Learn how to leverage your DevOps foundation for success in building Internal Developer Platforms (IDPs). ## Transforming optimization into an invisible platform capability DevFeed: [Transforming optimization into an invisible platform capability]() Original publisher: [Read original article]() Author: Graziano Casto Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Optimization](), [Platform Engineering](), [finops](), [SRE](), [Automation](), [GitOps](), [Cloud](), [dashboards](), [Deployment](), [Kubernetes]() Tags: [automation](), [cloud](), [cloud-native](), [cost](), [dashboards](), [deployment](), [developer](), [developer-experience](), [finops](), [kubernetes](), [optimization](), [performance](), [platform](), [platform-engineering](), [sre]() ### AI overview The article argues that cloud-native organizations should transform optimization from a manual, reactive activity into an invisible platform capability. It focuses on reducing friction among FinOps professionals, SREs, and developers by continuously balancing cost, performance, and reliability through automation and GitOps. It also highlights the day-two operational gap and the limitations of passive monitoring dashboards and static golden paths. ### Source excerpt Break the FinOps/SRE/Developer friction by transforming optimization into an automated, invisible platform capability. Achieve continuous cost and performance balance through GitOps ## Doing the right thing when things go wrong DevFeed: [Doing the right thing when things go wrong]() Original publisher: [Read original article]() Author: Mark Gorman Published: 2026-07-14T13:41:39Z Content type: article Language: en Sources: [The Intercom Blog]() Topics: [incident](), [incident management](), [SRE](), [Slack]() Tags: [engineering](), [incident](), [incident-management](), [metrics](), [safety](), [slack](), [sre](), [support](), [work]() ### AI overview Fin's incident-response process focuses on quickly detecting customer-impacting disruptions, bringing the right people together, reducing impact, communicating clearly, and learning from what happened. The article emphasizes that incident management is a shared discipline supported by tools such as Slack channels, status pages, runbooks, and SRE tooling. ### Source excerpt When customers rely on you, minutes matter in an incident. Here's the process Fin's engineers follow to detect, mitigate, and learn from every one. ## How Sherlocks AI uses Temporal to orchestrate AI agents for incident resolution DevFeed: [How Sherlocks AI uses Temporal to orchestrate AI agents for incident resolution]() Original publisher: [Read original article]() Author: Akshat Sandhaliya Published: 2026-07-07T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [AI Agent](), [incident](), [SRE](), [observability](), [Cloud](), [tracing](), [Graphs](), [Databases](), [Kubernetes]() Tags: [ai-agent](), [ai-agents](), [apm](), [community](), [dashboards](), [databases](), [incident](), [kubernetes](), [logs](), [monitoring](), [observability](), [sre](), [temporal]() ### AI overview Sherlocks AI describes using Temporal Cloud to run durable AI-agent investigations for production incidents. The platform also uses workflows for knowledge-graph updates, infrastructure scans, and event ingestion, with retries, checkpointing, and parallel execution to improve reliability. ### Source excerpt Sherlocks AI on how Temporal Cloud runs durable AI agent investigations, infra scans, Knowledge Graph updates, and event ingestion for SRE teams. ## Behind the Flame: Pierson Mayhew DevFeed: [Behind the Flame: Pierson Mayhew]() Original publisher: [Read original article]() Author: Megan Batterbury Published: 2026-06-04T14:00:00Z Content type: article Language: en Sources: [The incident.io Blog]() Topics: [incident management](), [Artificial Intelligence](), [SRE](), [Slack]() Tags: [ai](), [emea](), [enterprise](), [incident](), [incident-channel](), [incident-management](), [incident-response](), [outage](), [post-mortem](), [slack](), [slack-incident](), [sre]() ### AI overview This profile introduces Pierson Mayhew, an Enterprise and Strategic Account Executive at incident.io. He describes enterprise sales, collaboration across the company, developing the go-to-market strategy in EMEA, and his excitement about AI SRE and its potential impact on incident management. ### Source excerpt Meet Pierson Mayhew, Enterprise/Strategic Account Executive here at incident.io. 🔥 ## Customers over control: how we measure On-call reliability DevFeed: [Customers over control: how we measure On-call reliability]() Original publisher: [Read original article]() Author: Mike Fisher Published: 2026-05-28T16:29:00Z Content type: article Language: en Sources: [The incident.io Blog]() Topics: [SRE](), [Availability](), [site-reliability-engineering](), [nginx](), [Amazon Web Services](), [API](), [Network](), [incident]() Tags: [2025](), [api](), [availability](), [aws](), [customers](), [http](), [incident](), [incident-channel](), [incident-management](), [incident-response](), [load-balancer](), [metrics](), [network](), [on-call](), [outage](), [post-mortem](), [slack-incident](), [sre]() ### AI overview This article explains how incident.io measures the reliability of its On-call product from the customer's perspective. It focuses on two critical functions, defines SLIs and monthly SLOs, and describes monitoring at the GCP load balancer, alerting, replicated components, and lessons from an AWS outage. ### Source excerpt Instead of thinking about reliability as an exercise in figuring out what we can control, and ignoring anything beyond that, we think about what we'll be really proud to offer to customers. ## Razorpay Oncall Agent: From 30-Minute Investigations to 90-Second AI Analysis DevFeed: [Razorpay Oncall Agent: From 30-Minute Investigations to 90-Second AI Analysis]() Original publisher: [Read original article]() Author: Anuj Gupta Published: 2026-04-29T06:56:11Z Content type: article Language: en Sources: [Razorpay Engineering - Medium]() Topics: [Artificial Intelligence](), [Langgraph](), [incident](), [SRE](), [observability](), [Large Language Model](), [Retrieval-Augmented Generation]() Tags: [agent](), [ai](), [incident](), [langgraph](), [logs](), [metrics](), [observability](), [retrieval-augmented-generation](), [sre]() ### AI overview Razorpay describes building a multi-agent AI system to automate production incident investigations. The Oncall Agent uses LangGraph, an LLM, alerting tools, and two retrieval-augmented generation systems containing architecture, dependency, and diagnostic runbook context. ### Source excerpt Our on-call engineers were spending 30 minutes investigating every production alert. Here's what happened when we automated it. At 3 AM, alerts don't care about your sleep schedule. When our payment infrastructure threw an error last month, our on-call engineer spent 32 minutes jumping between six different monitoring systems before understanding what was broken. One tool for metrics. Another for logs. Third tool for pod health. And multiple more for infrastructure, deployment history and database health. By the time they identified the root cause (a bad deployment), payment failures had already impacted customers for nearly 40 minutes. This wasn't their fault. They followed our runbook perfectly. The problem was that no single system could tell them "here's what's wrong and why." They had to manually connect dots across disconnected observability tools. That's when we asked ourselves: what if AI could do this investigation for us? The Metric Nobody Optimizes For The SRE world talks endlessly about Mean Time to Detect (how fast you catch problems) and Mean Time to Resolve (how fast you fix them). But there's a critical phase hiding between them: Mean Time to Investigate. MTTI is the gap from "we know it's broken" to "we know what to fix." At Razorpay, this phase was consuming 20-40 minutes per incident. With 15-20 incidents weekly, that's 6-8 hours of engineering time spent doing repetitive investigative work. Worse, the quality was inconsistent. Senior engineers knew exactly which systems to check for payment alerts. Junior engineers sometimes checked irrelevant dashboards or missed critical correlations. The investigation depended entirely on who was on-call that night. What We Built (And Why It Works) Razorpay Oncall Agent is a multi-agent AI system that automates incident investigation. The architecture is built on LangGraph, a framework for creating stateful workflows with conditional logic, and uses LLM as the reasoning engine. Here's how the components work t ## How it feels to run an incident with Investigations DevFeed: [How it feels to run an incident with Investigations]() Original publisher: [Read original article]() Author: Chris Evans Published: 2026-04-23T18:06:25Z Content type: article Language: en Sources: [The incident.io Blog]() Topics: [incident](), [User experience (UX)](), [Artificial Intelligence](), [SRE](), [Slack]() Tags: [ai](), [incident](), [incident-channel](), [incident-management](), [incident-response](), [outage](), [post-mortem](), [slack](), [slack-incident](), [sre](), [ux]() ### AI overview This article walks through using incident.io's Investigations, described as an AI SRE and investigation engine, during a real incident. It focuses on improving the incident-response experience through ergonomic UX, automated investigation work, and Slack-based coordination. ### Source excerpt For the last 18 months, we've been building Investigations and one of the things we've learned is that UX matters more than you think. This week, I used AI SRE to run a real incident, and I walk you through it end-to-end. ## Software engineer interviews for the age of AI DevFeed: [Software engineer interviews for the age of AI]() Original publisher: [Read original article]() Author: hi@swizec.com (Swizec Teller) Published: 2026-03-25T00:00:00Z Content type: opinion Language: en Sources: [Swizec Teller]() Topics: [Artificial Intelligence](), [coding](), [SRE](), [systems]() Tags: [ai](), [coding](), [interviews](), [software-engineer](), [sre](), [system-design]() ### AI overview This opinion article argues that software engineer interviews should account for AI-assisted coding while still testing practical experience, depth of project knowledge, system design, and willingness to take responsibility for reliable production systems. It recommends repeatable, low-noise evaluation processes with multiple interviewers and detailed follow-up questions. ### Source excerpt Maybe AI will replace engineers, I don't know. Self-driving cars were just around the corner for 50 years. Until then we've got shit to do and engineers to hire. ## Scaling Autonomous Site Reliability Engineering: Architecture, Orchestration, and Validation for a 90,000+ Server Fleet DevFeed: [Scaling Autonomous Site Reliability Engineering: Architecture, Orchestration, and Validation for a 90,000+ Server Fleet]() Original publisher: [Read original article]() Author: Najmus Saqib Published: 2026-03-13T15:49:48Z Content type: article Language: en Sources: [DigitalOcean]() Topics: [SRE](), [Artificial Intelligence](), [AI Agent](), [Large Language Model](), [web applications](), [Monitoring](), [PHP](), [Debian]() Tags: [agents](), [ai](), [ai-agent](), [article](), [debian](), [engineering](), [hosting](), [llms](), [monitoring](), [php](), [sre](), [web-applications]() ### AI overview The article describes how Cloudways developed an AI SRE agent to help investigate and troubleshoot incidents affecting web applications. It discusses the monitoring layer, control plane, Insight Generation Engine, and the agent's use of Cloudways-specific Debian customizations. ### Source excerpt As Cloudways scaled from a bootstrapped startup to a leading managed PHP hosting service, one of the biggest challenges we encountered was the growing support load. Managing a fleet of over 90,000 servers and half a million applications means thousands of support requests, requiring a team of hundreds of human support agents. The rise of LLMs and AI agents provided an ideal opportunity to rethink our support operations. Early on, we recognized that an AI-based SRE agent could significantly reduce the burden on our support teams. At Cloudways, we deeply care about our customers' applications and websites because they are the backbone of their businesses and livelihoods. Every minute of downtime matters, and our priority has always been to ensure their apps come back online as quickly as possible. An AI SRE agent helps customers to receive timely, in-depth investigation and troubleshooting for their web applications delivering faster diagnosis and quicker resolution. Cloudways Copilot, an AI-powered Site Reliability Engineer in its current state is a result of over a year of constant efforts to achieve these goals. It has features like Insights and SmartFix which provide users access to a detailed diagnosis and resolution steps for web apps incidents. These AI-powered insights are significantly faster and more consistent than those provided by a human agent. View Wistia video How does CW Copilot work? The monitoring layer continuously observes each user machine for Webstack issues and excessive. When an anomaly is detected, it triggers an alert and forwards it to the control plane. The control plane then routes the alert to the Insight Generation Engine, which consists of following components: AI SRE Agent The effectiveness of an AI agent depends heavily on the context it is provided. The agent is made aware of the customizations done by Cloudways on top of Debian so that it can work in an optimized way. It includes details like: File structure details (e.g., where co ## Scaling Whatnot: Behind the Largest Live Shopping Stream in US History DevFeed: [Scaling Whatnot: Behind the Largest Live Shopping Stream in US History]() Original publisher: [Read original article]() Author: Whatnot Engineering Published: 2026-02-24T14:33:16Z Content type: article Language: en Sources: [Whatnot Engineering]() Topics: [Scalability](), [Architecture & Design](), [Platform Engineering](), [SRE](), [Elixir](), [Python]() Tags: [cloud-computing](), [devops](), [elixir](), [infrastructure](), [load-testing](), [python](), [resilience](), [scalability](), [scale](), [site-reliability-engineer](), [software-engineering](), [sre](), [technology]() ### AI overview Whatnot describes how it prepared its platform for a MrBeast giveaway stream that reached 583,000 concurrent viewers and became the largest live shopping event in US history. The article covers architectural investments, progressive production load testing, event-day results, and lessons for future scalability. ### Source excerpt On February 8, 2026, over a half million viewers tuned in to watch MrBeast give away 1 million dollars in prizes on Whatnot. On Big Game Sunday 2026, MrBeast went live on Whatnot for a giveaway show that would become the largest live shopping event in US history. At peak, 583,000 concurrent viewers were watching a single show on our platform. Over 555k people entered a single giveaway. We drove hundreds of thousands of new signups in 24 hours. If any one of a dozen systems buckled, it would have happened live on camera. We pulled it off with zero major incidents. But that outcome was never guaranteed. It took months of preparation, 60+ engineers across every major engineering org, and some of the most significant infrastructure investments we've ever made. In this post, we'll walk through the biggest technical challenges we faced and how we solved them, not with throwaway scaffolding, but with durable platform improvements that raise our scalability ceiling for every seller and buyer on the platform. We'll cover the work in three parts. First, the key architectural investments we made to handle this scale: admission control, connection pooling, feed resilience, and video infrastructure. Then, how we validated it all through progressive production load testing. Finally, what happened on event day, what we learned, and what we're carrying forward. Setting the Stage If you've followed our blog, you might remember our Post Malone "Post-Poned" post from 2022 or our three-part series on preparing for the 2024 Big Game. Each of those events pushed us to improve, and each one revealed new limits. As our community has grown, scaling our infrastructure to match has been a consistent priority. The MrBeast event was on a different order of magnitude entirely, but it accelerated work that was already underway. Our target was to support 1 million concurrent viewers on a single stream and 1.35 million across the platform. To put that in perspective, our previous largest event had ## Users buy your service, not your code DevFeed: [Users buy your service, not your code]() Original publisher: [Read original article]() Author: hi@swizec.com (Swizec Teller) Published: 2026-02-18T00:00:00Z Content type: opinion Language: en Sources: [Swizec Teller]() Topics: [reliability](), [SRE](), [distributed-systems](), [Artificial Intelligence](), [ai-coding](), [Software as a service]() Tags: [ai](), [ai-agents](), [ai-coding](), [distributed-systems](), [engineering](), [podcast](), [reliability](), [saas](), [sre]() ### AI overview A podcast discussion about operating software services when AI can write code. It emphasizes that users value reliable services, and covers AI coding workflows, production ownership, useful logs, distributed-systems debugging, AI SRE agents, accountability, and SLAs. ### Source excerpt You might enjoy this podcast episode. Sylvain and I talked about owning production in a world where AI writes the code. [Next page]()