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The tech world has been building up towards the shift to a fully agentic development life cycle for a few years now. AI is changing how software gets built. Across the Puppet ecosystem, we’re seeing a shift toward more agentic engineering workflows. AI helps generate code, shape documentation, and accelerate how Puppet modules evolve.

Ask an AI for K-pop concert advice without saying the group, city, date, or budget. It may confidently send you to a BLACKPINK tribute night in Cleveland with a $400 resale ticket. The AI was plenty confident. It just had nothing real to go on. That is exactly what happens when developers test AI applications with invented traffic. The test may look reasonable. The result may even pass.

Stripe Projects reduces the manual work of setting up, managing, and paying for third-party SaaS solutions. You can now use it to get started with Datadog in just two commands: If your Stripe account has a verified email address, running those commands in the Stripe CLI gives you a Datadog organization with a 14-day free trial and an automatically generated API key that is ready to use. You avoid email verification loops, tab-switching to copy an API key out of a dashboard, and lengthy sign-up forms.

If your team has adopted AI coding agents, you've probably noticed something strange: writing code stopped being the hard part. That's the shift Patrick, a principal engineer at Upsun, kept returning to in our latest Product Highlights conversation. He's spent twelve years here, most of them writing back-end APIs in Go, and the past year building with AI on our newest product, Upsun Dispatch. His verdict on where the bottleneck moved is blunt: "The code isn't really a problem anymore.

Organizations are increasingly using multiple models to build AI agents in order to find the best balance of performance and cost for each agentic task and LLM call. As we discovered in the 2026 State of AI Engineering report, there isn’t currently a clear winner in terms of adoption among competing models and many organizations are keeping older models in flight despite frequent new releases.

We've been making a series of improvements across Obkio’s Network Monitoring and Observability application, and a lot of that work has been focused on one goal: simplifying not just how we show network performance data, but how easy it is to actually interpret and understand that data. Not everyone monitoring a network has the time, or the networking background, to dig through graphs line by line to figure out what's going on.

Most CVE remediation tools can tell you that a vulnerability could be exploited. Few can confirm whether it actually is. A scanner flags the same CVE in two services and marks both as vulnerable. Only one of them ever runs the flawed code in production. That gap, reachable in theory versus reachable in fact, is the real problem, and static analysis alone cannot close it.

In December 2025, an AI coding agent at AWS suddenly decided to delete and rebuild an entire production environment, causing a 13-hour service disruption and a PR headache for Amazon. As rapid adoption of AI leads to more high-profile, revenue-impacting incidents, resilience has moved from a technical concern to a board-level financial risk.