# self-healing Published articles for self-healing. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Agent Night demo recap: Brian Douglas's self-healing Pokémon agent DevFeed: [Agent Night demo recap: Brian Douglas's self-healing Pokémon agent]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-17T18:34:12Z Content type: news Language: en Sources: [WorkOS Blog]() Topics: [Demo](), [observability ai agents](), [telemetry](), [Claude Code](), [Python](), [DuckDB](), [Kafka](), [Code](), [Open Source]() Tags: [agent](), [claude-code](), [code](), [coding](), [demo](), [duckdb](), [kafka](), [open-source](), [python](), [self-healing](), [telemetry](), [traces]() ### AI overview A recap of Brian Douglas's Agent Night demo shows how Paper Compute ran Pokémon as a self-healing agent for six months. The setup uses Claude Code, a headless Python emulator, and traces from each session and turn. The article explains how labeled traces and per-session reflections can make the agent's history searchable and useful for improvement. ### Source excerpt Brian Douglas of Paper Compute closed Agent Night with six months of running Pokémon as a self-healing agent: traces, anomalies, dreams, and inceptions. ## CI Doesn't Need On-Demand: Moving Our Build Pipelines to Spot Instances DevFeed: [CI Doesn't Need On-Demand: Moving Our Build Pipelines to Spot Instances]() Original publisher: [Read original article]() Author: Yuvraj Singh Singhel Published: 2026-08-05T15:07:35Z Content type: article Language: en Sources: [Razorpay Engineering - Medium]() Topics: [CI/CD](), [GitHub Actions](), [Kubernetes](), [Amazon Web Services]() Tags: [aws](), [ci](), [ci-cd](), [github-actions](), [kubernetes](), [pipelines](), [self-healing]() ### AI overview Razorpay describes a self-healing infrastructure layer for GitHub Actions on Kubernetes that runs most CI workloads on AWS Spot Instances. The system detects spot-node termination, retries jobs, and cleans up orphaned pods; the article states that 80% of CI workloads run on spot instances with a 99.2% job success rate. ### Source excerpt Contributors: Guptaanuj CI/CD pipelines have always had a money-vs-stability problem. Run on-demand AWS instances and your build infrastructure is rock solid, expensive, and predictable. Run on spot instances and your costs drop 70-90%, but AWS can pull the rug with 2 minutes of warning. For most teams, this is a false choice. Either pay full price for reliability, or save money and accept that builds will fail in ways nobody can debug. At Razorpay, we stopped accepting that trade-off. We built a self-healing infrastructure layer for GitHub Actions on Kubernetes that runs 80% of our CI workloads on spot instances while maintaining a 99.2% job success rate. When AWS terminates a spot node mid-build, our system detects it, retries the job, cleans up the orphaned pods, and the developer never knows. This is the story of what we built, why polling wasn't an option, and the war stories that taught us how to do retries without burning everything down. The Problem With Spot Instances On CI Spot instances are AWS capacity that nobody else wants right now. The pricing is brutal compared to on-demand: a c5.2xlarge that costs around $0.34/hour on-demand drops to roughly $0.08/hour on spot. For workloads like CI/CD, where jobs are short-lived and parallelizable, the math is obvious. The catch is in the contract. AWS reserves the right to take spot capacity back at any moment, with a 2-minute warning. That works for some workloads. For others, it's catastrophic. GitHub Actions runners on Kubernetes is the hard case. A typical CI job goes like this: GitHub assigns the job to a runner. The runner is a pod on a Kubernetes cluster running on an AWS Spot instance. The job downloads dependencies, runs tests, builds artifacts. The runner reports back to GitHub. Now insert a spot termination at minute 4 of a 7-minute build. What happens? The runner pod dies mid-job. GitHub never gets a completion signal; the job hangs until timeout, then marks as "failed". A new runner pod gets schedule ## Building self-healing feature releases with Harness FME metric alerts and Event Relay DevFeed: [Building self-healing feature releases with Harness FME metric alerts and Event Relay]() Original publisher: [Read original article]() Author: Joshua Klein Published: 2026-07-24T00:00:00Z Content type: tutorial Language: en Sources: [Harness Blog]() Topics: [releases](), [feature flags](), [Automation](), [experiments](), [Resilience](), [Monitoring]() Tags: [automation](), [event](), [feature](), [feature-flags](), [metrics](), [monitoring](), [payload](), [pipelines](), [resilience](), [self-healing](), [webhooks]() ### AI overview This tutorial explains how to connect Harness FME metric alerts to Event Relay triggers so a pipeline can automatically mitigate risky feature releases through actions such as killing a feature flag. The pattern separates signal emission, webhook handling, and controlled remediation. ### Source excerpt Learn how to build self-healing feature releases with Harness Feature Management & Experimentation. Connect metric-alert webhooks to Event Relay triggers and au | Blog ## Self-healing GPU nodes in Kubernetes: What we learned building the EKS node monitoring agent DevFeed: [Self-healing GPU nodes in Kubernetes: What we learned building the EKS node monitoring agent]() Original publisher: [Read original article]() Author: Sajjan Gundapuneedi Published: 2026-07-19T13:00:00Z Content type: article Language: en Sources: [Kubernetes Overview, News and Trends | The New Stack]() Topics: [Kubernetes](), [Amazon EKS](), [GPU](), [Monitoring]() Tags: [ai-infrastructure](), [amazon-eks](), [aws-marketplace](), [gpu](), [kubernetes](), [monitoring](), [open-source](), [pcie](), [post-contributed](), [self-healing](), [sponsor-aws-marketplace](), [sponsored-post-contributed]() ### AI overview The article describes how the EKS Node Monitoring Agent detects Kubernetes node failures, writes NodeConditions, and works with Karpenter to replace affected nodes. It explains design lessons from operating automatic repair at GPU scale and places the agent within EKS Auto Mode. ### Source excerpt When you run Kubernetes at the scale we do on Amazon EKS, nodes break constantly. GPUs fall off the PCIe The post Self-healing GPU nodes in Kubernetes: What we learned building the EKS node monitoring agent appeared first on The New Stack. ## Durable Execution with DBOS and CockroachDB DevFeed: [Durable Execution with DBOS and CockroachDB]() Original publisher: [Read original article]() Author: Amine El Kouhen, Ph.D. Published: 2026-07-08T00:00:00Z Content type: tutorial Language: en Sources: [Cockroach Labs]() Topics: [CockroachDB](), [AI Agent](), [Orchestration](), [Database](), [Python](), [TypeScript]() Tags: [agents](), [ai-agent](), [cockroachdb](), [database](), [orchestration](), [python](), [self-healing](), [typescript]() ### AI overview This article explains how DBOS provides durable execution for long-running AI agent workflows by embedding workflow state persistence directly in a Python or TypeScript application database. It contrasts this approach with Temporal's separate orchestration service and describes pairing DBOS with CockroachDB. ### Source excerpt Modern AI applications are no longer single-shot inference calls. They are long-running agents that plan, act, observe, and retry across time. ## Kubernetes Tutorial: Deploy, Expose, Scale, and Self-Heal Your First App DevFeed: [Kubernetes Tutorial: Deploy, Expose, Scale, and Self-Heal Your First App]() Original publisher: [Read original article]() Author: Nimesha Jinarajadasa Published: 2026-07-06T08:25:48Z Content type: tutorial Language: en Sources: [Kubernetes - KodeKloud Blog | DevOps, Cloud, Kubernetes, AI Tutorials & More]() Topics: [Kubernetes](), [Tutorial](), [kubectl](), [YAML](), [Containers](), [Deployment]() Tags: [beginners](), [declarative](), [guide](), [kubectl](), [kubernetes](), [scale](), [self-healing](), [state](), [tutorial](), [yaml]() ### AI overview A beginner-friendly, hands-on tutorial that walks through deploying an application to a Kubernetes cluster, exposing it with a Service, scaling it, observing self-healing, and repeating the deployment declaratively with YAML and kubectl. ### Source excerpt A hands-on Kubernetes tutorial for beginners: deploy your first app, expose and scale it, see self-healing, and use YAML with kubectl. ## Security Baked Into the JVM: why fork Apache River and OpenJDK? DevFeed: [Security Baked Into the JVM: why fork Apache River and OpenJDK?]() Original publisher: [Read original article]() Author: Peter Firmstone Published: 2026-06-28T00:00:00Z Content type: opinion Language: en Sources: [Nicolas Fränkel]() Topics: [Security](), [Java](), [openjdk](), [Architecture & Design](), [Remote Procedure Call (RPC)](), [Networks]() Tags: [authorization](), [dirtychai](), [discovery](), [distributed](), [ipv6](), [java](), [jgdms](), [jini](), [jvm](), [lock-free](), [microservices](), [openjdk](), [rpc](), [security](), [self-healing](), [technical]() ### AI overview This article introduces DirtyChai, a community fork of OpenJDK that restores Java authorization infrastructure, and JGDMS, a security-hardened fork of Apache River for dynamically discoverable microservices over IPv6. It argues that distributed systems require security beyond network firewalls and outlines the projects' complementary roles, including authorization, service discovery, hardened deserialization, transport security, proxy trust verification, and codebase safety checks. ### Source excerpt The more distributed a system, the harder it is to secure. Code crosses JVM boundaries. Objects are serialized across trust boundaries. Third-party proxies run inside your process. The usual answer is a network firewall. It helps, but it operates at the wrong level. Java 17 deprecated the SecurityManager, Java 24 put the final nail in its coffin. Most developers didn't notice. ## A Five-Pattern Playbook for Running Parallel AI Coding Agents with Pulumi DevFeed: [A Five-Pattern Playbook for Running Parallel AI Coding Agents with Pulumi]() Original publisher: [Read original article]() Author: Engin Diri Published: 2026-06-02T00:00:00Z Content type: tutorial Language: en Sources: [Pulumi]() Topics: [AI-assisted coding](), [ai-coding](), [Git](), [GitHub](), [Pull Request]() Tags: [ai](), [ai-agents](), [ai-coding-agents](), [automation](), [best-practices](), [coding-agents](), [concurrent](), [git](), [github](), [infrastructure-as-code](), [platform-engineering](), [pull-request](), [pulumi-neo](), [self-healing]() ### AI overview The article presents a five-pattern playbook for running multiple AI coding agents concurrently. It uses issues as specifications, a plan/build/validate workflow, isolated Git worktrees, fresh-session review, and a self-healing layer. The approach is described for application code, with infrastructure work identified as a further question. ### Source excerpt AI coding has two shapes right now. One agent in a loop, sequential work, you babysitting the chat window. Call that 2x. Most teams live here. Five agents in worktrees, parallel work, fresh-context review on every change. Call that 10x. The trick: 2x is mostly prompting, 10x is mostly plumbing. The parallel coding playbook is a five-pattern setup for running multiple AI coding agents at the same time without them stepping on each other: an issue used as the spec, a plan/build/validate loop, parallel git worktrees, fresh-session review, and a self-healing layer. The whole thing targets application code. The interesting question, and the one I keep ending up at, is what changes when the five agents are touching infrastructure. 2x is prompting, 10x is plumbing 2x is one human, one agent, one repo, one branch. The agent writes, you review, you tell it to try again, it tries again. The bottleneck is your attention. Whatever the agent's raw throughput, your reading speed sets the ceiling. 10x moves you out of the per-change loop and into the issue loop. You write five issues with sharp acceptance criteria, send each one to its own agent in its own worktree, and let them plan, build, and validate end-to-end. You read five PRs at lunch instead of pair-programming on one all morning. Concurrent isolation does the work. And isolation is mostly an infrastructure problem. The five pillars The five pillars, in one sentence each. Issue is the spec. The GitHub issue carries the acceptance criteria. The pull request is the artifact that gets validated. Input and output of every implementation are versioned, scoped, and reviewable on their own. Plan, build, validate. Three stages, three artifacts. A markdown plan you can read in thirty seconds. A build that produces a diff. A validate step that checks the diff against the spec. Parallel worktrees. Each agent runs in its own git worktree so concurrent changes never trample each other. One repo, five working trees, five branches. Fres ## How does the Kubernetes controller manager work? DevFeed: [How does the Kubernetes controller manager work?]() Original publisher: [Read original article]() Author: Daniele Polencic Published: 2026-04-13T00:00:00Z Content type: tutorial Language: en Sources: [Learnk8s]() Topics: [Kubernetes](), [Deployment]() Tags: [controllers](), [kubernetes](), [running](), [self-healing](), [state](), [yaml]() ### AI overview A tutorial explaining how the Kubernetes controller manager runs reconciliation loops that watch resources and move the cluster toward its desired state. It demonstrates how a Deployment and ReplicaSet replace a deleted Pod and contrasts declarative control with imperative scripting. ### Source excerpt The controller manager runs the control loops that make Kubernetes self-healing. Learn how controllers watch resources, reconcile desired state, and drive the chain reaction from Deployment to running Pods. ## How Nx Connects Local AI Agents to CI for Autonomous Workflows DevFeed: [How Nx Connects Local AI Agents to CI for Autonomous Workflows]() Original publisher: [Read original article]() Author: Juri Strumpflohner Published: 2026-02-03T00:00:00Z Content type: article Language: en Sources: [Juri Strumpflohner]() Topics: [AI Agent](), [ci](), [Loop Engineering](), [MCP](), [MCP Server](), [Testing](), [Tooling]() Tags: [agent](), [ai](), [ai-agents](), [autonomous](), [ci](), [claude-code](), [development](), [mcp-server](), [nx](), [self-healing](), [testing]() ### AI overview This article explains how Nx connects local AI agents to Nx Cloud CI through the CI Monitor skill and Nx MCP server. The workflow lets an agent monitor CI, receive failure context, communicate with Nx's Self-Healing CI agent, apply verified fixes, and continue iterating until CI is green. ### Source excerpt Learn how Nx bridges the gap between local AI agents and CI, enabling fully autonomous development workflows with the ci-monitor skill and Self-Healing CI. ## DriftlessAF: Introducing Chainguard Factory 2.0 DevFeed: [DriftlessAF: Introducing Chainguard Factory 2.0]() Original publisher: [Read original article]() Published: 2026-01-29T00:00:00Z Content type: release Language: en Sources: [Chainguard: Unchained]() Topics: [chainguard](), [Artificial Intelligence](), [Automation](), [Bot](), [Open Source](), [event driven](), [legacy]() Tags: [agentic](), [ai](), [architecture](), [automation](), [bots](), [chainguard](), [chainguard-containers](), [chainguard-factory](), [chainguard-images](), [chainguard-libraries](), [cve](), [driftlessaf](), [event-driven](), [legacy](), [open-source](), [reconciliation](), [safe-source-for-open-source](), [self-healing]() ### AI overview Chainguard announces Chainguard Factory 2.0, powered by the DriftlessAF agentic framework. The system uses AI-driven, self-healing reconciliation to build and maintain more than 2,000 zero-CVE images, and DriftlessAF is being open-sourced. ### Source excerpt Chainguard Factory 2.0, powered by DriftlessAF, brings AI-driven, self-healing reconciliation to build and maintain 2,000+ zero-CVE images -- now open sourced. ## What's New in Nx Self-Healing CI DevFeed: [What's New in Nx Self-Healing CI]() Original publisher: [Read original article]() Author: Juri Strumpflohner Published: 2025-10-14T00:00:00Z Content type: release Language: en Sources: [Juri Strumpflohner]() Topics: [ci](), [AI-assisted coding](), [nx-cloud](), [GitHub]() Tags: [ai-assisted-coding](), [ci](), [content](), [github](), [nx](), [nx-cloud](), [self-healing]() ### AI overview Nx announced updates to Self-Healing CI, including GitHub pull-request comments with proposed fixes, an improved Nx Cloud diff view, task-selection controls, and options to apply or reject AI-generated fixes. ### Source excerpt Enhanced GitHub integration, interactive diffs, fine-grained task control, auto-apply fixes, and local fix application--discover the latest improvements to Nx Self-Healing CI. ## The unbreakable web: From fragile scripts to bulletproof Workflows DevFeed: [The unbreakable web: From fragile scripts to bulletproof Workflows]() Original publisher: [Read original article]() Author: Kyle Jeong Published: 2025-08-20T00:00:00Z Content type: opinion Language: en Sources: [Temporal Blog]() Topics: [Browser Automation](), [Automation](), [Artificial Intelligence](), [AI Bots]() Tags: [agents](), [ai-agents](), [announcements](), [automation](), [browser](), [execution](), [self-healing](), [workflows]() ### AI overview The article argues that production web automation needs more than brittle scripts. It presents Browserbase's Stagehand SDK and Temporal Durable Execution as a combination for semantic interaction with web elements, recovery from failures, and continuation after interruptions. ### Source excerpt Build bulletproof browser automation and AI agents with Browserbase and Temporal. Stagehand and Durable Execution turn fragile scripts into resilient Workflows. ## The Principle of Reconciliation DevFeed: [The Principle of Reconciliation]() Original publisher: [Read original article]() Published: 2025-07-14T00:00:00Z Content type: opinion Language: en Sources: [Chainguard: Unchained]() Topics: [systems](), [Resilience](), [Kubernetes](), [Concurrency]() Tags: [architecture](), [concurrency](), [kubernetes](), [reconciliation](), [resilience](), [retry](), [self-healing](), [software-development](), [state](), [systems]() ### AI overview The article presents reconciliation as a feedback loop that observes actual state, compares it with a desired state, and acts to close the gap. It explains how Kubernetes controllers, workqueues, retries, bounded concurrency, and idempotency support resilient, self-healing systems. ### Source excerpt The priniciple of reconciliation is what makes systems self-healing, adaptable, and robust in the face of change and failure. ## Introducing Self-Healing CI for Nx and Nx Cloud DevFeed: [Introducing Self-Healing CI for Nx and Nx Cloud]() Original publisher: [Read original article]() Author: Juri Strumpflohner Published: 2025-06-23T00:00:00Z Content type: release Language: en Sources: [Juri Strumpflohner]() Topics: [ci](), [AI Agent](), [Cloud]() Tags: [agents](), [ai](), [ai-agents](), [bugs](), [ci](), [code](), [configuration](), [development](), [flaky](), [nx](), [nx-cloud](), [self-healing](), [teams](), [test](), [tests](), [validation](), [workflow]() ### AI overview Nx introduces Self-Healing CI for Nx Cloud, using AI agents to detect, analyze, fix, and validate CI failures while keeping developers in control through review and approval. The system complements existing flaky-task retries by addressing failures that require code, configuration, or dependency fixes. ### Source excerpt Introducing Nx Cloud Self-Healing CI: AI agents that automatically detect, analyze, and fix your CI failures so you do not have to babysit PRs. ## Synchronous communication - circuit breaker and fallback DevFeed: [Synchronous communication - circuit breaker and fallback]() Original publisher: [Read original article]() Published: 2020-05-07T00:00:00Z Content type: tutorial Language: en Sources: [Paweł Chudzik]() Topics: [Development](), [Software Engineering](), [Network]() Tags: [behavior](), [communication](), [network](), [self-healing](), [services](), [software-development](), [systems]() ### AI overview This tutorial explains the circuit breaker and fallback communication patterns for preventing cascading failures and giving dependent services time to recover. It describes closed, open, and half-open states, then examines implementations from Hystrix and resilience4j using a controllable failing service. ### Source excerpt The circuit breaker is a communication pattern that helps to avoid cascading failure of the system and gives dependent services time to recover. Along with fallback values defined by the developer, it gives a pretty wide safe net when communication channel is broken. Read more