# reliability-engineering Published articles for reliability-engineering. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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... ## AI Reliability Engineering DevFeed: [AI Reliability Engineering]() Original publisher: [Read original article]() Author: Alex Ewerlöf Published: 2026-07-12T17:34:19Z Content type: opinion Language: en Sources: [Alex Ewerlof Notes]() Topics: [AI Engineering](), [site-reliability-engineering](), [Large Language Model](), [Generative AI](), [Software Engineering]() Tags: [ai](), [ai-engineering](), [generative-ai](), [llms](), [reliability-engineering](), [software-engineering](), [sre]() ### AI overview This article examines how site reliability engineering practices can be adapted for AI systems and AI-generated black boxes. It focuses on the need to run these systems predictably, securely, and at scale as large language models and their supporting harnesses become more capable. ### Source excerpt Why SRE is a key skill in the age of AI-generated black boxes and how to renovate the traditional toolbox for the new era ## Zero downtime Upgrade: Yelp's Cassandra 4.x Upgrade Story DevFeed: [Zero downtime Upgrade: Yelp's Cassandra 4.x Upgrade Story]() Original publisher: [Read original article]() Author: Mark Surnin and Muhammad Junaid Muzammil, Software Engineer Published: 2026-04-07T00:00:00Z Content type: article Language: en Sources: [Yelp]() Topics: [Apache Cassandra](), [upgrade](), [NoSQL](), [Database](), [observability](), [Kubernetes]() Tags: [2026](), [apache](), [cassandra](), [clusters](), [downtime](), [kubernetes](), [nosql](), [observability](), [reliability-engineering](), [upgrade]() ### AI overview Yelp's Database Reliability Engineering team describes upgrading more than a thousand Cassandra nodes from 3.11 to 4.1 on Kubernetes without downtime. The article covers the motivation, expected reliability and performance improvements, operational guardrails, certificate handling, repairs, logging, and compatibility work for related components. ### Source excerpt The Database Reliability Engineering team at Yelp seamlessly upgraded more than a thousand Cassandra nodes with zero downtime. This post takes you behind the scenes of our upgrade strategy, from planning sessions to flawless rollouts. Background Motivation Apache Cassandra is a distributed wide-column NoSQL datastore and is used widely at Yelp for storing both primary and derived data. Yelp orchestrates Cassandra clusters on Kubernetes with the help of operators, as explained in our operator overview post. Upgrading from Cassandra 3.11 to 4.1 offered several observability and reliability improvements, in addition to performance gains. Based on public benchmarks, we expected to... ## Reliability Engineering for Air-Gapped Systems DevFeed: [Reliability Engineering for Air-Gapped Systems]() Original publisher: [Read original article]() Author: Alex Ewerlöf Published: 2026-04-03T21:03:06Z Content type: article Language: en Sources: [Alex Ewerlof Notes]() Topics: [observability](), [systems](), [Resilience](), [on-prem](), [Security](), [Requirements]() Tags: [observability](), [on-prem](), [reliability-engineering](), [resilience](), [security](), [supply-chain-attacks](), [systems](), [work]() ### AI overview This article discusses reliability engineering for air-gapped systems where software teams cannot access runtime data, metrics, or logs. It describes using SLI and SLO practices, alerting, secure operational processes, and on-premises maintenance to manage reliability in systems isolated from the internet. ### Source excerpt Tips and tricks to work around inaccessible observability ## Instrumenting AI Agents: Why the Apology Metric Is a First Class Reliability Signal DevFeed: [Instrumenting AI Agents: Why the Apology Metric Is a First Class Reliability Signal]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-03-21T00:00:00Z Content type: article Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Artificial Intelligence](), [AI Agent](), [reliability](), [agent observability](), [context]() Tags: [ai](), [ai-agents](), [api](), [backend](), [context](), [dependency](), [distributed-systems](), [inference](), [json](), [kv-cache](), [latency](), [llm](), [llmops](), [metric](), [observability](), [orchestration](), [rag](), [reliability](), [reliability-engineering](), [signal](), [timeout]() ### AI overview This article argues that AI agents can return HTTP 200 responses while still failing business tasks. It proposes tracking apology responses as a reliability signal to expose context starvation caused by stalled dependencies, timeouts, stale synchronization, dropped data, or payload truncation in retrieval and orchestration pipelines. ### Source excerpt An AI agent that returns "I apologize, but I cannot find that order" ships an HTTP 200. Infrastructure dashboards call that healthy; the business calls it a failure. Tracking apology phrases as a first-class SLO surfaces context starvation, a stalled dependency, a timeout guard tripping, or silent payload truncation, none of which HTTP status codes and CPU graphs can see. ## Multi-Agent System Reliability DevFeed: [Multi-Agent System Reliability]() Original publisher: [Read original article]() Author: Alex Ewerlöf Published: 2026-02-19T20:41:37Z Content type: tutorial Language: en Sources: [Alex Ewerlof Notes]() Topics: [Multi Agent Systems](), [LLMs](), [Artificial Intelligence](), [systems]() Tags: [architecture](), [llms](), [multi-agent](), [parallel](), [reliability-engineering](), [systems-engineering]() ### AI overview This article presents four architecture patterns--hierarchy, consensus, adversarial debate, and knock-out--to improve the reliability of multi-agent systems. It explains that underlying LLMs remain prone to hallucination, logical fallacies, and context drift, and that multi-agent architectures can amplify these errors and make debugging harder. ### Source excerpt 4 patterns to tame multi-agent systems for reliability ## Breaking the Loop: How we migrated our backup catalog for 250+ MySQL clusters to AWS DevFeed: [Breaking the Loop: How we migrated our backup catalog for 250+ MySQL clusters to AWS]() Original publisher: [Read original article]() Author: Ioannis Androulidakis Published: 2026-02-19T14:19:52Z Content type: article Language: en Sources: [Booking.com Development - Medium]() Topics: [migration](), [Amazon RDS](), [MySQL](), [Amazon Web Services](), [backups](), [Cloud](), [on-prem]() Tags: [amazon-rds](), [aws](), [backup](), [cloud](), [cloud-migration](), [data-backup](), [database](), [databases](), [migration](), [mysql](), [on-prem](), [relational-databases](), [reliability-engineering]() ### AI overview Booking.com's Database Engineering team describes migrating the backup catalog orchestrator's backend for more than 250 production MySQL clusters from a self-managed on-premises MySQL database to managed Amazon RDS for MySQL in AWS. The article discusses the migration's challenges, implemented solutions, and lessons learned. ### Source excerpt This post was originally published internally on May 9, 2025 and has been reworked for a public audience. Authors: Ioannis Androulidakis, Mohammed Gaafar Intro The Database Engineering team at Booking.com is constantly looking for ways to improve database reliability and support scaling our business. In 2025 we completed a major milestone in our effort to modernize our infrastructure: we seamlessly migrated the backend of the orchestrator that schedules and manages the daily backups of 250+ production MySQL clusters. More specifically, we moved from a self-managed MySQL database running on premises to a managed Amazon RDS MySQL database running in the cloud. This blog post delves into the challenges we faced, the solutions we implemented, and some key lessons we learned along the way. We are moving to the cloud (gradually) Cloud adoption is a journey, not a switch. Over the past few years the adoption of AWS solutions at Booking.com has been growing rapidly across different business units, allowing teams to be more autonomous, run their databases in the cloud and solve known issues with their old on-premise setup. Like customer teams, we are running our own databases to power the core services that we offer and manage databases efficiently at scale. To name a few: automatic failover of writable primaries, auto-scaling of read-only replicas, service discovery, capacity planning, online schema changes, user access management, volume backups, etc. Traditionally, the Database Engineering team has been running databases on-premises. In the emerging era of cloud databases, we wanted to compare our in-house offerings with existing cloud databases and bridge the gap between the two. Knowing that we lacked deep, hands-on experience with the operational realities of running critical infrastructure on AWS, we saw this as a unique opportunity to learn more about cloud databases while also improving the reliability of our systems. Long story short, we decided to prioritize the m ## Office Hours with Engineering Managing Director Mae Santos DevFeed: [Office Hours with Engineering Managing Director Mae Santos]() Original publisher: [Read original article]() Author: Emily Majewski Published: 2024-06-26T17:34:49Z Content type: article Language: en Sources: [Two Sigma Engineering]() Topics: [reliability](), [systems](), [Data pipelines](), [Resilience](), [Automation]() Tags: [automation](), [best-practices](), [collaboration](), [data-pipelines](), [engineering](), [integrity](), [reliability-engineering]() ### AI overview In this Office Hours interview, Two Sigma engineering leader Mae Santos discusses her career, leadership values, and responsibilities overseeing critical applications and data reliability engineering. She emphasizes integrity, collaboration, resilience, and designing reliability into applications and data systems from the beginning. ### Source excerpt The post Office Hours with Engineering Managing Director Mae Santos appeared first on Two Sigma.