Embedding Governance in Agentic AI Workflows

AI agents can take action. That changes everything - and most teams aren't ready for it. That's why we started Humans in the Loop - a video series for engineers and DevOps teams figuring out the agentic AI era without losing control of their systems. The first episode is out. Andrey Devyatkin and Fernando Gonçalves set the stage: what agentic AI actually means, why context is everything in infrastructure troubleshooting, and what tools like Cursor, MCP, Claude Code, and Amazon Q CLI mean for DevOps engineers today. https://lnkd.in/eiSbrP7Y

We are making a series of videos explaining concepts critical to understand when it comes to agentic AI and its application in DevOps. If you are starting up with magnetic AI those episodes are for you. We will transition to more advanced topics as we finish establishing a shared glossary and a common understanding. Stay tuned for more!

We’re entering an era of code abundance. With AI generating more code than ever, the developer’s role is shifting from writing every line to curating, validating, and confidently shipping the right code. The result of this avalanche of code? Testing becomes the bottleneck. That's why we created CloudBees Smart Tests, which uses AI/ML to predict which tests matter most so teams can focus on risk, not volume. The outcome: faster feedback, leaner pipelines, and greater confidence in every release. Read more about Smart Tests on DevOps.com 🔗 https://lnkd.in/e9QqFgwf

AI is showing up everywhere in DevOps—but rarely as a connected system. Code, testing, deployment, and ops are evolving with AI, but the real challenge is how they come together in production. On April 25, Harness and The AI Collective are bringing together practitioners shipping AI across the lifecycle and navigating real production complexity. Register to hear what actually works when AI moves into production: https://lnkd.in/gZZsJvKt

AI is showing up everywhere in DevOps—but rarely as a connected system. Code, testing, deployment, and ops are evolving with AI, but the real challenge is how they come together in production. On April 25, Harness and The AI Collective are bringing together practitioners shipping AI across the lifecycle and navigating real production complexity. Register to hear what actually works when AI moves into production: https://lnkd.in/gNAhE-3S

You can't optimize what you can't measure. codeburn is a terminal dashboard that shows exactly what your AI coding tools are costing. I share tools like this every week in DevOps Bulletin. #devops #ai #opensource

AI won't replace your CI/CD pipeline. But it will act as your smartest gatekeeper. 🤖 Integrating AI into DevOps isn't just a buzzword; it's solving real, everyday engineering bottlenecks: ⚡ Predictive Test Selection: Instead of waiting an hour for a massive test suite to run, machine learning models analyze the specific lines of code you changed and only run the relevant tests. Hours drop to minutes. 🧠 Intelligent Code Reviews: The potential here is massive. Instead of just checking syntax, AI can review a Pull Request for context, logic flaws, and output a plain-English summary of the impact before a human ever looks at it. 🛡️ Automated Canary Analysis: Tying your observability stack (like Prometheus) into an AI analyzer means deployments can automatically roll back the second a metric spikes, long before an engineer gets paged. For the aspiring DevOps engineers my advice is this: Your declarative Git repo and your pipeline are still the unshakeable source of truth. AI is simply your high-speed navigator. Are any of your teams actively using AI tools inside your CI/CD workflows yet (like predictive testing or log summarization)? Let's hear what's actually working in the wild! 👇 #DevOps #CICD #ArtificialIntelligence #CloudEngineering

Many organisations are investing heavily in AI but are still struggling to move models from pilot into production and scale them effectively. In this clip from our recent MLOps & DevOps webinar, Jonathan Ede shares insights from recent industry reports on the current state of AI in the enterprise and why MLOps has become more important than ever in turning experimentation into real, scalable impact. Download the full session here: https://lnkd.in/eJAwaQHe #EnterpriseAI #AIInProduction #MLOps #DevOps #AIAtScale

AI is showing up everywhere in DevOps—but rarely as a connected system. Code, testing, deployment, and ops are evolving with AI, but the real challenge is how they come together in production. On April 25, Harness and The AI Collective are bringing together practitioners shipping AI across the lifecycle and navigating real production complexity. Register to hear what actually works when AI moves into production: https://lnkd.in/gaYwbhJJ

DevOps Days Buenos Aires always shows what teams are actually dealing with in production. Less theory, more “this broke, here’s what we learned.” Our team was on the ground last week, talking to engineers, hearing real GitOps pain, and seeing how people are handling scale, visibility, and tool sprawl in practice. AI came up in a lot of talks. Not as hype, but as an operational challenge. How do you run it on top of existing infra without adding even more complexity? That’s exactly the problem space we’re building in with Kunobi Visual GitOps without breaking how teams already work Adding a short clip below to give a feel for the atmosphere 👇 #DevOpsDays #Kubernetes #GitOps #PlatformEngineering #DevOps #CloudNative #DevOpsDaysBuenosAires #AI #AIInfrastructure #MLOps