# on call Published articles for on call. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## A working incident response model for GPU clouds DevFeed: [A working incident response model for GPU clouds]() Original publisher: [Read original article]() Author: Sridhar Rajarao Published: 2026-09-12T00:00:00Z Content type: article Language: en Sources: [Sridhar Rajarao]() Topics: [incident](), [Incident response](), [GPU](), [Monitoring](), [Tooling]() Tags: [communication](), [debugging](), [gpu](), [gpu-cloud](), [grafana](), [incident](), [incident-management](), [incident-response](), [jira](), [management](), [on-call](), [operations](), [ownership](), [pagerduty](), [review](), [slack](), [sre]() ### AI overview This article presents an incident response model for GPU clouds and other customer-facing infrastructure businesses. It emphasizes preparation, named ownership, meaningful alert paths, incident command, separation of technical work from customer communication, and post-incident learning. It argues that tools such as PagerDuty, Jira, Grafana, and Slack are useful only within a clear operating model. ### Source excerpt The tools matter, but they only work when they sit inside a clear operating model: ownership, signal, command, communication, and learning. ## Every service needs an owner DevFeed: [Every service needs an owner]() Original publisher: [Read original article]() Author: Sridhar Rajarao Published: 2026-09-12T00:00:00Z Content type: article Language: en Sources: [Sridhar Rajarao]() Topics: [systems](), [incident]() Tags: [catalog](), [customer](), [incident](), [on-call](), [ownership](), [platform-engineering](), [production](), [reliability](), [service](), [service-catalog](), [sre](), [startups](), [team]() ### AI overview The article argues that growing organizations need a focused service catalog to make production ownership visible. It recommends recording each service's customer outcome, owning team, current on-call contact, deployment path, health dashboard, runbook, and dependencies, and maintaining those records as part of engineering work. ### Source excerpt A useful service catalog is not an inventory project. It is a public record of who owns a customer outcome when the system is healthy and when it fails. ## From noise to signal: Monitoring Amazon DocumentDB like a pro DevFeed: [From noise to signal: Monitoring Amazon DocumentDB like a pro]() Original publisher: [Read original article]() Author: Deepak Deepesh Published: 2026-09-09T15:23:20Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [Amazon DocumentDB](), [site-reliability-engineering](), [Amazon CloudWatch Logs](), [Database]() Tags: [amazon](), [amazon-cloudwatch](), [amazon-cloudwatch-logs](), [amazon-documentdb](), [best-practices](), [database](), [devops](), [expert-400](), [health](), [how-to](), [logs](), [metrics](), [monitoring](), [on-call](), [operations](), [performance](), [production]() ### AI overview A tutorial on monitoring production Amazon DocumentDB clusters with tiered alerts, CloudWatch metrics, Performance Insights, profiler logs, slow-query diagnosis, and garbage-collection health checks. ### Source excerpt Learn how to build a tiered alerting strategy for Amazon DocumentDB by organizing CloudWatch metrics into Critical, Warning, and Advisory tiers, diagnosing slow queries through three complementary lenses, and monitoring garbage collection health before it becomes a cluster-wide incident. ## Classify Domain and System Errors to Set HTTP Status, Logging, and Paging DevFeed: [Classify Domain and System Errors to Set HTTP Status, Logging, and Paging]() Original publisher: [Read original article]() Author: The T-Shaped Dev Published: 2026-08-29T04:19:38Z Content type: tutorial Language: en Sources: [The T-Shaped Dev]() Topics: [Code](), [HTTP](), [API]() Tags: [api](), [code](), [errors](), [graphql](), [http](), [logs](), [middleware](), [on-call]() ### AI overview This tutorial distinguishes expected domain errors, such as invalid input or forbidden actions, from unexpected system errors, such as database failures, upstream timeouts, and memory exhaustion. It proposes encoding the distinction in error types so middleware can determine the HTTP status, user message, log level, and paging behavior. ### Source excerpt One distinction that sets your HTTP status, your log level, and who gets paged. (6 min) ## ScyllaDB Customer Experience Spotlight: Susie Solis DevFeed: [ScyllaDB Customer Experience Spotlight: Susie Solis]() Original publisher: [Read original article]() Author: Cynthia Dunlop Published: 2026-08-10T14:28:35Z Content type: article Language: en Sources: [ScyllaDB]() Topics: [Databases](), [distributed-systems](), [cloud-infrastructure](), [Kubernetes](), [Terraform](), [Latency](), [Linux]() Tags: [blog](), [cloud-infrastructure](), [community](), [customer](), [cx-profiles](), [database](), [distributed-systems](), [kubernetes](), [latency](), [linux](), [on-call](), [production](), [streaming](), [support](), [terraform]() ### AI overview Susie Solis, a ScyllaDB Technical Support Engineer, discusses her background in technical support, cloud infrastructure, and distributed systems, along with a Kubernetes and Terraform project built to reproduce environments and troubleshoot customer issues. She also describes ScyllaDB's ability to sustain high-throughput production workloads and low latency during major streaming events. ### Source excerpt Meet Susie Solis, a Technical Support Engineer on the Customer Experience team here at ScyllaDB. ## AI SRE: Cut MTTR in Half with Autonomous Incident Resolution DevFeed: [AI SRE: Cut MTTR in Half with Autonomous Incident Resolution]() Original publisher: [Read original article]() Author: Matar Peles Published: 2026-08-10T11:34:38Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [site-reliability-engineering](), [incident](), [Artificial Intelligence](), [monitor](), [dashboards](), [Traces](), [archive search](), [Pull Request](), [codex](), [cursor]() Tags: [agent](), [ai](), [codex](), [cursor](), [dashboards](), [datadog](), [incident](), [monitor](), [monitoring](), [on-call](), [sre](), [traces](), [workflow]() ### AI overview This developer article describes an autonomous incident-resolution workflow built in Port. It argues that incident response time is largely spent reconstructing context from alerts, logs, dashboards, traces, ownership data, recent changes, and runbooks. The proposed agent-based workflow aims to use that full context to triage, diagnose, and fix production incidents, with a reported 50% reduction in MTTR. The supplied text ends during a discussion of why simply routing alerts to coding agents can fail. ### Source excerpt See how an SRE agent workflow cuts MTTR in half, using full context to triage, diagnose, and fix incidents autonomously, all the way to a RCA. ## Eric Schwartz on what it takes to run an AI SRE at petabyte scale DevFeed: [Eric Schwartz on what it takes to run an AI SRE at petabyte scale]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-07T00:00:00Z Content type: article Language: en Sources: [WorkOS Blog]() Topics: [Artificial Intelligence](), [site-reliability-engineering](), [data-platforms](), [telemetry](), [observability ai agents]() Tags: [ai](), [data](), [data-platforms](), [on-call](), [production](), [root-cause-analysis](), [sre](), [telemetry]() ### AI overview An interview with Traversal product manager Eric Schwartz examines how the company operates an AI site reliability engineer for large enterprises. The article explains that petabyte-scale telemetry requires continuously analyzing, compressing, and indexing data ahead of runtime, with an SRE-focused tool and prompt harness. Traversal reports that deployments are generally running in production within a week with minimal tuning, supported by forward-deployed engineering for last-mile optimization. ### Source excerpt Traversal PM Eric Schwartz on data platforms, routing models by severity, and the permission ladder toward self-driving production, from AI Engineer 2026. ## Prioritize security findings with the Datadog Runtime Prioritization Engine DevFeed: [Prioritize security findings with the Datadog Runtime Prioritization Engine]() Original publisher: [Read original article]() Author: Christina DePinto; Lucas Maley; Leo Wang Published: 2026-07-31T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Vulnerabilities](), [telemetry](), [dashboards]() Tags: [agent](), [ai](), [cloud](), [cloud-security](), [collaboration](), [dashboards](), [on-call](), [security](), [telemetry](), [vulnerabilities](), [vulnerability-management]() ### AI overview Datadog explains how its Runtime Prioritization Engine uses telemetry and security signals to infer ownership and prioritize cloud security findings affecting critical resources. ### Source excerpt Learn how the Datadog Runtime Prioritization Engine infers ownership and identifies business-critical resources to help you prioritize security findings. ## Behind the Flame: Nicole Hussein DevFeed: [Behind the Flame: Nicole Hussein]() Original publisher: [Read original article]() Author: Megan Batterbury Published: 2026-07-30T14:00:00Z Content type: article Language: en Sources: [The incident.io Blog]() Topics: [incident]() Tags: [collaboration](), [customers](), [engineering](), [incident](), [incident-channel](), [incident-management](), [incident-response](), [on-call](), [outage](), [platform](), [post-mortem](), [product](), [slack-incident](), [technical]() ### AI overview This Behind the Flame profile introduces Nicole Hussein, a Product Engineer at incident.io. It describes her work across product specifications, technical scoping, implementation, customer conversations, and incident-related features, including customizable alert-message templates and ongoing development of the on-call product. ### Source excerpt Meet Nicole Hussein, Product Engineer here at incident.io. 🔥 ## 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. ## How internal developer portals improve incident management DevFeed: [How internal developer portals improve incident management]() Original publisher: [Read original article]() Author: Sooraj Shah Published: 2026-07-22T11:46:50Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [internal developer portal](), [incident management](), [Platform Engineering](), [sdlc](), [Monitoring]() Tags: [developer](), [developer-portal](), [incident-management](), [metrics](), [monitoring](), [on-call](), [platform-engineering](), [real-time](), [sdlc]() ### AI overview This article explains how internal developer portals can strengthen incident management by bringing service ownership, dependencies, monitoring information, infrastructure health metrics, and operational actions into one place. It emphasizes better context, autonomy, and efficiency for on-call engineers, including self-service playbooks for incident remediation. ### Source excerpt Boost your incident management program: Upgrade from tools to a developer portal for better context, autonomy, and effectiveness. ## How An Incident Agent Would Handle A Port Incident DevFeed: [How An Incident Agent Would Handle A Port Incident]() Original publisher: [Read original article]() Author: Zohar Einy Published: 2026-07-22T11:10:11Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [incident](), [Artificial Intelligence](), [Automation](), [Kafka](), [DevOps](), [Slack](), [GitHub]() Tags: [agent](), [ai](), [article](), [automation](), [autonomous](), [building](), [config](), [customer](), [devops](), [github](), [incident](), [kafka](), [on-call](), [production](), [scale](), [slack](), [ui](), [work]() ### AI overview The article examines a real incident at Port in which three teams independently investigated the same production problem after a customer generated 1.7 million automation runs in 90 minutes. It describes duplicated investigation, ineffective service restarts, fragmented communication, and slow manual coordination, then considers how an incident agent could connect signals and support autonomous resolution. ### Source excerpt Explore how AI could have handled a real incident at Port, improving response times, automation, and decision-making in production. ## Introducing the new Vercel Agent DevFeed: [Introducing the new Vercel Agent]() Original publisher: [Read original article]() Author: Amelia Charles Published: 2026-07-21T04:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Vercel](), [Security](), [Deployment](), [Pull Request](), [dashboards](), [SIEM, Security, Observability](), [Command-line interface](), [Grafana](), [Caching](), [ci]() Tags: [agent](), [agents](), [caching](), [cli](), [cost](), [deployment](), [errors](), [github](), [logs](), [metrics](), [on-call](), [performance](), [production](), [pull-request](), [security](), [vercel]() ### AI overview Vercel expands Vercel Agent with a dashboard-based production investigation and response workflow. The agent can inspect logs, metrics, and deployments, answer project questions, review pull requests, diagnose cost increases and failed builds, and propose fixes. It is read-only by default, operates under its own identity, and requires approval before changing production or opening corrective changes. ### Source excerpt Today we're expanding Vercel Agent. It started by triaging alerts and reviewing your pull requests. Now it has a home in your dashboard, where it can investigate production, answer questions about your projects, and take action once you approve it. Because Vercel Agent is built into the platform that deploys and runs your app, when something changes in production, it's your first responder. It autonomously investigates your logs, metrics, and deployments, finds the root cause, and proposes a fix, before you've opened your laptop. Vercel Agent works under its own identity and is read-only by default. You can reach it through the Vercel Dashboard, GitHub, and the CLI. What Vercel Agent does for you We've been running Vercel Agent on our production deployments for months. This is what a typical investigation looks like. A bad deploy ships at 11pm and the checkout endpoint starts throwing 500s. By the time the on-call engineer logs in, Vercel Agent has already traced the errors to the deploy that shipped four minutes earlier, and recommends an instant rollback. The engineer approves the plan. With permission, Vercel Agent rolls back to the previous production deployment and begins working on a PR to fix the endpoint. The time from alert to mitigated: less than three minutes. What you can do with Vercel Agent You can also direct Vercel Agent yourself. Hand it a task and it does the legwork, then either answers your question or hands you a fix to approve. It never changes production on its own. For example: Review a pull request. Tell it to look at a PR and it flags performance regressions and risky changes that a passing CI run won't show. Trace a cost increase. Ask why your bill jumped and it finds the culprit, like a code change that server-renders a page on every request instead of caching. With approval, it writes the fix and opens a PR. Fix a broken build. Point Vercel Agent at a failed deployment and it reads the logs, finds the failing config, asks permission to u ## Runbook Best Practices and Automated Incident Response with Harness AI SRE DevFeed: [Runbook Best Practices and Automated Incident Response with Harness AI SRE]() Original publisher: [Read original article]() Author: Ryan Taylor Published: 2026-07-15T00:00:00Z Content type: tutorial Language: en Sources: [Harness Blog]() Topics: [site-reliability-engineering](), [Incident response](), [Automation]() Tags: [automation](), [best-practices](), [how-to](), [incident](), [incident-response](), [on-call](), [operations](), [sre](), [sre-automation]() ### AI overview This tutorial explains how to create effective software-system runbooks that are actionable, accessible, accurate, authoritative, and adaptable. It also describes how Harness AI SRE can automate runbook execution during incidents, including filing tickets, triggering rollbacks, and posting incident-timeline updates. ### Source excerpt Learn what makes a runbook effective, how to keep them accurate and actionable, and how Harness AI SRE automates runbook execution during incidents. | Blog ## How Instacart Built Blueberry, a Slack-Native On-Call Reasoning Harness DevFeed: [How Instacart Built Blueberry, a Slack-Native On-Call Reasoning Harness]() Original publisher: [Read original article]() Author: Karthik Halukurike Published: 2026-07-14T16:39:20Z Content type: tutorial Language: en Sources: [Instacart]() Topics: [incident](), [Slack](), [context](), [dashboards]() Tags: [ai-agent](), [ai-agents-in-action](), [context](), [incident-response](), [on-call](), [reasoning](), [slack](), [software-development]() ### AI overview Instacart describes Blueberry, a Slack-native on-call reasoning harness that auto-triages qualifying alerts and supports follow-up investigations. It is designed to help teams reach a shared understanding and test theories more quickly during on-call work. ### Source excerpt How we built a Slack-native on-call reasoning harness at Instacart that shortens time to first insight, speeds up theory testing, and turns tribal knowledge into reusable infrastructure. Key Contributors: Karthik Halukurike, Gabe de Oliveira, Hassan Jallad, Alan Wong On-call work is a race to turn noisy signals into shared judgment. The hardest minutes of that race aren't the ones spent fixing the bug -- they're the ones spent figuring out what the bug even is, while everyone in the thread is asking the same question from a slightly different angle. Blueberry is the system we built at Instacart for those several minutes. It lives in the Slack thread where the team is already coordinating, picks up each alert as it fires, and lands a grounded explanation back in the thread in about three minutes -- thousands of times a month. On-call is mostly a clarity and speed problem Most on-call pain does not start with a major incident. It starts in the noisy window right after a page fires, when the thread fills with links, dashboards, guesses, and partial context. Someone asks whether a deployment caused it. Someone else asks how broad it is. A third engineer joins and asks what is going on. In that moment, the hardest problem is often not deep root-cause analysis. It is getting to a shared understanding quickly enough that the team can make a good next move. For us, that made two metrics matter most: time to first insight (TTFI) and time to test theories (TTTT). TTFI comes from auto-triaging every qualifying alert the moment it lands. TTTT comes from engineer-initiated on-demand investigations and follow-ups as the investigation deepens. Blueberry is a Slack-native on-call reasoning harness Blueberry was built for that first window of confusion. Blueberry lives in Slack because that is where on-call engineers already coordinate, ask clarifying questions, and make decisions under pressure. We wanted the system to work inside the shared operational conversation so the full team ## Eliminating Java cold starts with AWS Lambda Managed Instances DevFeed: [Eliminating Java cold starts with AWS Lambda Managed Instances]() Original publisher: [Read original article]() Author: Jay Colodner Published: 2026-07-13T20:23:19Z Content type: article Language: en Sources: [AWS Compute Blog]() Topics: [AWS Lambda](), [benchmarking](), [Amazon Web Services](), [Amazon EC2](), [Spring Boot 4](), [cpu](), [SDKs]() Tags: [aws](), [aws-lambda](), [benchmark](), [benchmarking](), [benchmarks](), [comparison](), [cpu](), [deployment](), [java](), [jit](), [latency](), [on-call](), [performance](), [spring-boot-4](), [uncategorized]() ### AI overview This article benchmarks four Java deployment modes for AWS Lambda: Standard Lambda, SnapStart, GraalVM Native Image, and Lambda Managed Instances. Using identical Spring Boot applications across CPU-bound, I/O-plus-computation, and I/O-bound workloads, it reports that Managed Instances preserve JVM state and enable JIT optimization, improving median and tail latency while reducing cold-start spikes. ### Source excerpt A single cold start can push your Java Lambda function's response time from milliseconds to seconds, enough to violate your p99 SLA, timeout a downstream service, and page your on-call. The Java Virtual Machine (JVM) performs best in long-running processes. Its Just-In-Time (JIT) compiler progressively optimizes code over thousands of invocations. Standard serverless execution environments [...] ## Cache layer architecture: a practical guide to speed & scale DevFeed: [Cache layer architecture: a practical guide to speed & scale]() Original publisher: [Read original article]() Author: Jim Allen Wallace Published: 2026-07-04T00:00:00Z Content type: tutorial Language: en Sources: [Redis Blog]() Topics: [Caching](), [App](), [Database](), [data](), [client](), [Edge](), [HTML]() Tags: [app](), [architecture](), [browser](), [cache](), [caching](), [close-to-users](), [content](), [data](), [database](), [e-commerce](), [guide](), [on-call](), [scale](), [speed](), [storage](), [systems](), [tech-de]() ### AI overview A practical guide to cache-layer architecture: how caches reduce database and storage latency, where client, edge, and application caches fit, and which operational concerns--including hit rate, cold-cache failures, and consistency--matter as traffic grows. ### Source excerpt Your app works fine with a thousand users. Then traffic spikes, requests hammer the same database systems, and response times crawl. A cache layer sits between your app and your slower data stores to absorb that load. Done well, it turns slow database... ## Behind the Flame: Ellie Cherrill DevFeed: [Behind the Flame: Ellie Cherrill]() Original publisher: [Read original article]() Author: Megan Batterbury Published: 2026-07-02T14:00:00Z Content type: article Language: en Sources: [The incident.io Blog]() Topics: [coding](), [incident](), [Slack]() Tags: [coding](), [cto](), [customers](), [incident](), [incident-channel](), [incident-management](), [incident-response](), [on-call](), [outage](), [post-mortem](), [slack](), [slack-incident]() ### AI overview This profile follows Ellie Cherrill, a Product Engineer at incident.io, describing her rapid-paced work, which is focused largely on coding and collaboration. She discusses customizing Slack alert messages for customers and contributing to the On-Call system, where changes must be assessed for their impact across the product. ### Source excerpt Meet Ellie Cherrill, Product Engineer here at incident.io. 🔥 ## De-risking a PagerDuty migration: the objections we hear most, and how to clear them DevFeed: [De-risking a PagerDuty migration: the objections we hear most, and how to clear them]() Original publisher: [Read original article]() Author: Eryn Carman Published: 2026-06-09T14:49:00Z Content type: article Language: en Sources: [The incident.io Blog]() Topics: [migration](), [incident](), [Tooling]() Tags: [engineering](), [incident](), [incident-channel](), [incident-management](), [incident-response](), [infrastructure](), [migration](), [on-call](), [organizational](), [outage](), [platforms](), [post-mortem](), [slack-incident](), [switching](), [technical]() ### AI overview This article explains why migrating from PagerDuty to another on-call platform is primarily an organizational and change-management challenge rather than a technical one. It presents seven common objections, including risk of missed alerts, and discusses how to address them through ownership, consensus-building, testing, and parallel operation before cutover. ### Source excerpt Often, switching on-call platforms isn't a technical challenge but a human one. In this post, we break down the seven objections engineering teams raise most often when considering a PagerDuty migration, and share exactly how to address each one. ## 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. ## Five rules for running an incident DevFeed: [Five rules for running an incident]() Original publisher: [Read original article]() Author: Sridhar Rajarao Published: 2026-05-27T00:00:00Z Content type: opinion Language: en Sources: [Sridhar Rajarao]() Topics: [incident](), [Learning]() Tags: [customer-experience](), [incident](), [incident-management](), [on-call](), [outage](), [postmortem](), [production](), [recovery](), [reliability](), [root-cause-analysis](), [rules](), [signal](), [speed](), [sre](), [team]() ### AI overview This opinion article presents five rules for handling production incidents: assess severity by customer impact, use an Incident Commander, mitigate before investigating root cause, maintain regular communication, and use postmortems for learning and accountable follow-up. ### Source excerpt The difference between a 10-minute incident and a 3-hour outage is rarely technical. Five things I wish every on-call team locked in before their first big page. ## Operational Readiness: The Review That Catches Problems DevFeed: [Operational Readiness: The Review That Catches Problems]() Original publisher: [Read original article]() Author: Sridhar Rajarao Published: 2026-05-25T00:00:00Z Content type: tutorial Language: en Sources: [Sridhar Rajarao]() Topics: [Development](), [incident](), [Artificial Intelligence]() Tags: [on-call](), [operational](), [ops](), [problems](), [production](), [reliability](), [sre](), [validation](), [verification]() ### AI overview An operational readiness review is useful only when checklist items are backed by evidence and verified before a service launches. The article explains common failure modes, recommends attaching artifacts to each item, and describes AI applications for evidence gathering, gap detection, and summary drafting while keeping the final readiness decision with human reviewers. ### Source excerpt A short, verifiable checklist for production launches. What to ask, why ORRs become theater, and where AI helps. ## If Hope is Your Strategy, You're Doing On-Call Wrong DevFeed: [If Hope is Your Strategy, You're Doing On-Call Wrong]() Original publisher: [Read original article]() Author: Natalia de Pablo Garcia Published: 2026-05-19T10:05:05Z Content type: article Language: en Sources: [Backend Job: skill, salary and insights - Codemotion Magazine]() Topics: [DevOps](), [Monitoring](), [site-reliability-engineering](), [Security]() Tags: [careers](), [devops](), [monitoring](), [on-call](), [operational](), [software-architecture]() ### AI overview The article promotes a Codemotion Madrid 2026 talk about improving on-call operations through proactive monitoring, well-defined runbooks, and intelligent alerting. It argues that these practices help engineering teams manage incidents and operate critical systems at scale, particularly in finance. ### Source excerpt Codemotion Madrid 2026 is fine-tuning every detail to welcome a new edition packed with knowledge, business opportunities, and networking. Among the wide range of topics ahead, DevOps will play a key role at a time when, although everything moves at breakneck speed, security and stability remain non-negotiable. In the talk "If Hope is Your Strategy,... Read more The post If Hope is Your Strategy, You're Doing On-Call Wrong appeared first on Codemotion Magazine. ## incident.io launches PagerDuty Rescue Program DevFeed: [incident.io launches PagerDuty Rescue Program]() Original publisher: [Read original article]() Author: Tom Wentworth Published: 2026-05-13T13:30:00Z Content type: release Language: en Sources: [The incident.io Blog]() Topics: [incident](), [Tooling](), [migration](), [Availability](), [Artificial Intelligence]() Tags: [ai](), [availability](), [dependency](), [engineering](), [incident](), [incident-channel](), [incident-management](), [incident-response](), [migration](), [on-call](), [outage](), [post-mortem](), [report](), [slack-incident](), [switching](), [tooling](), [uptime]() ### AI overview incident.io announced the PagerDuty Rescue Program for companies switching their on-call tooling. The program offers contract buyouts, AI-powered migration assistance, built-in migration tooling, a 99.99% uptime guarantee, and AI-first alert investigation. ### Source excerpt incident.io just launched the PagerDuty Rescue Program, making it easier than ever for engineering teams to ditch their decade-old on-call tooling. The program includes a contract buyout (up to a year free), AI-powered white glove migration, a 99.99% uptime SLA, and AI-first on-call that investigates alerts autonomously the moment they fire. [Next page]()