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gremlin

Optimizing Kubernetes pod deployments for reliability with topology spread constraints

Aug 13, 2026 By Andre Newman In Gremlin
If you’re like many Kubernetes users, you don’t pay much attention to where or how Kubernetes distributes your pods. As long as they’re running, it doesn’t matter where they get deployed, right? Surely Kubernetes will use some complex algorithm to figure out the most reliable way to distribute your pods across the cluster…right? Pod distribution plays a much bigger role in reliability than you might think.
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What your AI SRE can't see (and what you can do about it)

Aug 6, 2026 By Ryan Detwiller In Gremlin
AI SRE is having a moment. The category pulled in massive funding rounds over the last two years, Gartner published its first market guide, and vendors are promising everything from 90% faster resolution to fully autonomous incident response. If you run an engineering organization, someone has probably pitched you an AI SRE in the last quarter. And let’s be honest: faster triage, less alert fatigue, and automated frontline response are wins for understaffed teams.
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gremlin

Managing slow container starts with Kubernetes readiness probes

Aug 4, 2026 By Andre Newman In Gremlin
Imagine if your workday started as soon as you woke up. Before you can even start your coffee maker, email alerts are flooding in, coworkers are pinging you on Slack, and your phone is buzzing nonstop with reminders. You haven’t even pulled the covers back, and your boss is asking you about deliverables. This is what Kubernetes pods deal with every day. Unless, that is, you use readiness probes.
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The Gremlin app for Dynatrace: resilience testing and reliability scoring, built on the observability you already trust

Jul 28, 2026 By Ryan Detwiller In Gremlin
Dynatrace gives engineering teams deep, real-time visibility into every service they run. That visibility is the foundation of every effective reliability practice, and it's exactly the foundation Gremlin is built to extend. Once you can see how your distributed systems behave today, the next step is knowing how they'll behave under failure tomorrow—and to do it before those failures happen.
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Chaos Testing Just Got Easier | New ChaosHub & AI Prompt Library | Resilience Testing | Harness

Jul 22, 2026 By Harness In Harness
In this video, we explore two major improvements to the Harness Resilience Testing documentation designed to help you build and manage chaos experiments more efficiently. What's new: ChaosHub Integration AI Prompt Library for Harness MCP These updates make it significantly easier to discover chaos experiments and leverage AI throughout your chaos engineering workflow. If you're using Harness Resilience Testing, this walkthrough will help you get started quickly and make the most of the new documentation experience.
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gremlin

Eliminate Reliability Blind Spots in AWS, Azure, and GCP

Jul 14, 2026 By Andre Newman In Gremlin
Cloud resilience often feels like an uphill battle. When you’re overseeing hundreds of applications across different providers, identifying potential failure points manually is nearly impossible. You’re left trying to find the needle in a haystack—a needle that could take down your entire application at any moment. To truly protect your uptime, you have to break the cycle of reactive troubleshooting.
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More Resilience, Less Overhead: How to Modernize Disaster Recovery Testing

Jul 1, 2026 By Gremlin In Gremlin
• Disaster recovery planning is essential for ensuring digital services remain online in the face of catastrophic failures or outages. When a major digital infrastructure outage occurs, systems need to be set up to automatically respond and restore functionality as quickly as possible. But no matter how in-depth your disaster recovery plan is, it’s still only theoretical until it’s thoroughly tested under realistic failure conditions, which is why testing is often mandated by leadership and regulators.
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Creating an agentic feedback loop with reliability guardrails

Jun 25, 2026 By Gavin Cahill In Gremlin
Reliability guardrails help make sure that your applications stay reliable without slowing down. In an earlier blog, we went into why agentic AI development needs reliability guardrails. It went over how the increased speed of AI development demands automated guardrails to verify resilience and what kinds of tests these guardrails should cover. But that’s only the beginning. By themselves, guardrails act as a gate to ensure resilience mechanisms hold under rapid changes.
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Why agentic AI development needs reliability guardrails

May 15, 2026 By Gavin Cahill In Gremlin
AI has massively accelerated code deployment. In fact, since the introduction of agentic coding, GitHub has seen exponential growth in PRs, commits, and new repos. What they originally predicted would require 10X capacity, they’re now estimating it’s going to require 30X capacity, and the biggest driver is agentic development. Companies across industries are building agentic pipelines to ship features faster than ever before. That acceleration isn’t without risk.
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Learn these 4 Chaos Engineering Principles Before You Break Anything | Resilience Testing | Harness

May 5, 2026 By Harness In Harness
Want to start chaos engineering? Don't randomly break stuff and hope for the best. Real chaos engineering starts with defining your system's steady state metrics like latency, throughput, and error rates. Then you form a clear hypothesis about what should happen when failures occur. Next, you inject controlled failures, starting small with single pod kills or network drops, not production meltdowns. Finally, you limit the blast radius by running experiments in safe environments first.
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