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5 Ways Agentic AI is Redefining DevOps Architecture for Self-Healing CI/CD Systems 

In the past, the flaky test was a problem: A race condition, a timeout, an annoyance that needed to be rerun and forgotten. That’s no longer the case. As enterprises transition from deterministic applications to agentic AI, the flakiness problem has become a structural issue.   Old CI/CD systems rely on binary assertions: Assert X == Y. But with AI agents, the output isn’t Y; it’s Y-like answers. Run the same agent again, and it will likely produce two defensible but varying results. So, the test suite built on a scenario that no longer exists, calls this a failure.   DevOps teams and engineers don’t just face the challenge of building agents but also recreating the entire pipeline.    In this post, we will share  how agentic AI is transforming  the DevOps architecture for self-healing CI/CD.    What Does the Term “Agentic” Mean Here?    Agentic AI is an automated system capable of receiving a target state, sensing its surroundings using telemetry and APIs, reasoning about the act...
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JFrog Report Surfaces Need for Rapid DevSecOps Change in AI Era

A report published by JFrog finds that cybercriminals are now increasingly targeting the artificial intelligence (AI) tools and platforms used by application development teams. Based on an analysis of 18.2 billion artifacts managed via the JFrog Platform, security researchers discovered 969 AI agent skills carrying high-impact payloads in addition to 495 malicious AI models on the Hugging Face platform for hosting open source AI models. Additionally, 56 malicious extensions were also discovered on the OpenVSX registry. The survey also finds 41% of respondents work for organizations that are actively using AI libraries, with organizations on average employing 9.3 AI libraries each. At the same time, a separate global survey of 1,508 security and DevOps professionals conducted by JFrog finds more organizations are struggling to secure code generated by AI coding tools. Nearly half of respondents (45%) said reviewing and hardening AI-generated code is now a major time drain, with an eq...
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On-Call: The Silent Force Shaping Engineering Culture

There is a silent force shaping engineering culture inside every technology organization. It affects productivity, team morale, psychological safety, and long-term retention. And yet, it is rarely discussed in executive meetings or reflected in meaningful KPIs. That force is on-call. On-call is one of the most direct touchpoints engineers have with the reality of the systems they own. When it’s healthy, it builds confidence, resilience, and pride. When it’s unhealthy, it quietly corrodes everything that makes engineering teams effective. And while most companies drastically underestimate this effect, a recent survey found that on-call is the least-liked aspect of software engineering, often leading to burnout and attrition. Poorly managed on-call isn’t only a mental health issue; it can also impact a company’s brand and finances, as recent significant outages from AWS, Azure, and Cloudflare have shown. In this article, I will go over why on-call matters, the current challenges...
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Why DORA Metrics Look Different When AI Is Part of Your Development Workflow

DORA metrics have been a reliable compass for engineering teams for over a decade. Deployment frequency, lead time for changes, change failure rate, mean time to recovery, and reliability give teams a shared language for talking about delivery performance. The research behind them is solid, the benchmarks are well-established, and most engineering leaders know what good looks like for each metric. What is less discussed is how AI-assisted development changes the baseline assumptions those metrics were built on. Not whether DORA metrics are still relevant — they are — but how the same numbers can mean something different when a significant portion of your codebase is being generated by AI coding tools. Deployment Frequency Goes Up. Sometimes for the Wrong Reasons. AI coding assistants accelerate code production. Developers who use them ship features faster, close tickets quicker, and generate pull requests at a higher rate than before. For teams tracking deployment frequen...
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Perplexity Bumblebee Shakes Loose Hidden Threats on Dev Desktops

The fight to maintain security has moved to the engineer’s messy desktop.   Last week, AI search provider Perplexity open-sourced an internal tool, Bumblebee, for checking developer machines, either Linux or macOS, for vulnerable software. Continuous integration pipelines have baked security checks into them, with Software Bills of Materials (SBOMs) ensuring that the correct version of a package makes it to runtime. So malicious attackers are gravitating to the underbelly of enterprise security, the developer’s laptop.  Most developer machines are no doubt teeming with unpatched and outdated software, byproducts of various experiments and projects. There’s probably an outdated version of Node.js on most machines, or perhaps a never-used Warp terminal. Or maybe they downloaded a malware-infested package at some point, and it is just sitting on the hard drive waiting to be activated.   And certainly, many Perplexity engineers have plentiful recipes for agents lying around, which cou...
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Ten Great DevOps Job Opportunities

DevOps.com is now providing a weekly DevOps jobs report through which opportunities for DevOps professionals will be highlighted as part of an effort to better serve our audience. Our goal in these challenging economic times is to make it just that much easier for DevOps professionals to advance their careers. Of course, the pool of available DevOps talent is still relatively constrained, so when one DevOps professional takes on a new role, it tends to create opportunities for others. The ten job postings shared this week are selected based on the company looking to hire, the vertical industry segment and naturally, the pay scale being offered. We’re also committed to providing additional insights into the state of the DevOps job market. In the meantime, for your consideration. Dice Okta, Inc. San Francisco, CA Staff Site Reliability Engineer, TCore (FedRamp) $194,000 to $267,000 Lockheed Martin Corporation Annapolis, MD Senior DevOps Engineer – Clearance Require...
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Co-Developing an AI Native Observability Platform  

As AI capabilities continue to evolve, AI is becoming central to managing the growing complexity of distributed, hybrid enterprise environments, enabling more effective analysis, correlation, and automation across interconnected systems.   Traditional infrastructure and specifically network monitoring approaches, often built around siloed tools and static thresholds, struggle to keep pace with the scale, velocity, and interdependencies of modern systems. Further blurring the boundaries between network, application, and infrastructure domains makes it harder to isolate root causes and maintain operational resilience. In this context, AIOps platforms have emerged as one response to the growing need for integrated observability, automation, and data-driven decision-making.   At AI Field Day, Selector AI presented an AIOps platform, which can be considered a foundation for co-creating more adaptive and data-driven network operations. Rather than positioning it purely as a product choice,...
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