Posts Tagged platform-engineering

The IDE is dying, and so is tool calling. OpenAI is not going to win. And next year, you’re going to be shipping code that you’ve never reviewed before, even as an experienced engineer.

These are bold claims, but the way we use AI in 2026 for coding and agents is going to look completely different. In this post, I want to cover my predictions and why they matter right now for DevOps engineers. Some of these are definitely hot takes, but that’s what makes this conversation worth having.

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Every organization builds up knowledge over time: naming standards, compliance requirements, patterns your team has settled on, and proven approaches to common tasks. Until now, bringing this knowledge into Neo meant repeating it manually each time - specifying preferences, describing how your team works, and recreating prompts that someone already perfected.

Two new features change this. Custom Instructions teach Neo your standards so it applies them automatically. Slash Commands capture proven prompts so anyone on your team can use them with a keystroke.

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In 2026, several trends will dominate cloud computing, driving innovation, efficiency, and scalability. From Infrastructure as Code (IaC) to AI/ML, platform engineering to multi-cloud and hybrid strategies, and security practices, let’s explore the 10 biggest emerging trends.

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Kubernetes continues to evolve, powering not only applications but entire AI and ML systems across clouds, edges, and enterprises. By 2026, DevOps engineers, SREs, cloud engineers, and platform teams face growing pressure to deliver faster, smarter, and more secure infrastructure at scale.

Kubernetes automation is entering a new era where infrastructure as code, policy enforcement, and AI-driven orchestration work together to manage cloud environments intelligently.

Pulumi’s 2025 advancements, including Pulumi Kubernetes Operator 2.0 GA, new Kubernetes best practices playbooks, Pulumi Neo for AI assisted infrastructure management, and Policy Automation, set the foundation for a new era of Kubernetes automation that extends across every role involved in managing modern infrastructure.

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The era of AI-accelerated development has created a paradox: the faster developers move, the bigger the governance challenge becomes. For years, security and platform teams have worked to “shift left,” but the tools available have been incomplete. Most focus on detection, which is necessary but not sufficient. They identify thousands of policy violations across an organization’s infrastructure but leave teams with an overwhelming backlog and no scalable way to remediate it. This creates a persistent gap between finding a problem and fixing it. The result is an impossible choice between development velocity and organizational control, forcing leadership to slow down innovation to manage risk.

Today, we end that compromise.

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Platform teams publishing components to the Pulumi Private Registry can now see exactly which stacks are using each component and at which version.

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Ask a generic LLM to “fix my broken deployment,” and you’ll get generic advice. Ask Pulumi Neo the same question, and you’ll get a fix plan grounded in your actual infrastructure state.

The difference isn’t about better prompts or newer models. It’s about what the AI actually knows. Generic LLMs have been trained on the internet. Neo has been trained on your infrastructure.

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Welcome to the fourth post in our IDP Best Practices series. Today we’re diving into the world of drift detection and remediation, those critical day 2 operations that keep your infrastructure aligned with its intended configuration long after the initial deployment.

You’ve built a beautiful platform with robust guardrails, comprehensive templates, and well-defined golden paths. Your developers are productive, deployments are smooth, and everything seems perfect. Then reality hits. An on-call engineer makes an emergency change through the AWS console during a 3 AM incident. A team member tweaks a security group rule to debug a connection issue and forgets to revert it. Auto-scaling adjusts capacity based on load patterns. Before you know it, your actual infrastructure has quietly diverged from what your code describes.

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Neo just got significantly more capable. We’ve shipped three major updates: Operating Modes for flexible control, full ecosystem tool access, and Claude Sonnet 4.5 for better performance on complex infrastructure tasks.

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Since launching Pulumi Neo two weeks ago, we’ve seen platform teams discover creative ways to put their newest AI teammate to work. We have also been using Neo internally for a handful of use cases. Neo shifts the conversation from “what could AI do for infrastructure?” to “what can I actually accomplish with Neo today?”

The answer is quite a bit. Here are 10 concrete workflows that platform teams can use Neo for right now, each one designed to save hours of manual work while keeping humans in the driver seat.

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