According to Gartner Inc., 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% today. As organizations accelerate digital transformation, agentic AI in enterprise applications will move beyond individual productivity, setting new standards for teamwork and workflow through smarter human-agent interactions ...

AI investment is growing 52% year-over-year, yet progress is bumpy given data challenges and a skills gap that is holding businesses back. While developers are excited about the next model release, the reality is 99% of enterprises face AI project disruptions that are often not related to model choices that organizations often stress over. AI innovation hype is outpacing enterprise readiness, leaving developers with a tough choice: move fast on AI and risk failure, or move cautiously and watch competitors pull ahead. But what if there's a third option — one that requires no compromise? ...

AI-powered software innovation is generating annual savings of $28,249 per developer, according to GitLab's 2025 executive research report, which surveyed thousands of C-level executives from around the world. When implemented across the world's 27 million developers, AI's potential translates to more than $750 billion in global value annually ...

Widespread enterprise adoption has cemented artificial intelligence as an integral part of software design, development, and delivery. Relatively new to the scene, agentic AI is poised to double down on the speed and agility of simpler applications of AI, positioning it as a powerful business enabler. Recent research conducted by OutSystems revealed a clear trend: AI agents are maturing from experimental tools to central players in software development and business operations ...

In software development as in any aspect of business, time is money. Shortening development time to bring new applications to market is the goal of all commercial developers. Similarly, streamlining the development of custom software to optimize business processes can mean big savings. That's why more organizations are turning to coding shortcuts like artificial intelligence (AI) to slash development time ...

When was the last time you actually looked at the API calls in your codebase? Not the ones you wrote yourself, but the ones quietly generated by your AI assistant. Do you know where they point? Are they hitting a test server? Did they skip authentication? Are they leaking something in error responses? You start asking these questions after something goes wrong (and no one knows why) ... The thing is, generative AI (GenAI) is excellent at speeding up how we write code, but it could become a major concern if not thoroughly checked ...

A recent Palo Alto Networks report highlights the dual nature of GenAI tools: their success in areas like writing, testing, and deploying code, and the new risks they introduce, such as data exposure and malicious code generation. For DevOps teams, the key to success will be to leverage GenAI's power while ensuring control, security, and accountability ...

Sonar published a new study analyzing the quality and security of software code produced by top Large Language Models (LLMs), finding significant strengths as well as material challenges across tested models ...

Everyone is looking for new ways to use or integrate AI in their workflows, but not everyone is building to support its long-term use, according to the State of Development Report from Temporal Technologies. Only 1 in 4 respondents say their workflows operate smoothly, while others cite high overhead, brittle processes, and recovery issues that consume engineering time and slow teams down. The data points to growing operational strain and rising complexity as teams embrace AI, long-running systems, and multi-layered workflows ...

Five years in, Kubernetes is no longer an experiment — it's mission-critical infrastructure. The 2025 State of Production Kubernetes shows organizations doubling down on AI and edge, even while wrestling legacy VMs into their clusters. The companies that master scale and complexity fastest will create an unbeatable platform for innovation ...

AI is appearing everywhere in software development, from chatbots to code generation in internal tools. But while adoption is climbing, oversight often isn't. Teams are experimenting with large language models (LLMs) and Model Context Protocol (MCP) across organizations without clear guidelines or shared infrastructure, and that's a problem. This is especially urgent given interest in deploying AI agents as quickly as possible ...

The joy of coding isn't dead. But it's harder to find. Talk to most developers today, and you'll hear it — under the automation, the tooling, the race to ship. Something's missing. They're producing more than ever. But enjoying it less. It's not a productivity problem. It's a purpose problem. We've changed how software is built, but we haven't updated how developers experience the work. The role has shifted from creators to curators, from coders to conductors, and until we acknowledge that shift and design for it, we'll keep losing what once made the work meaningful ...