A GitLab survey of 3,266 DevSecOps professionals shows AI is boosting code creation but increasing the need for skilled engineers, compliance challenges and human oversight.

If you’ve been building artificial intelligence (AI) tools lately, you’ve probably noticed something: Your development workflow has become incredibly connected. Tools such as model context protocol (MCP) sit at the center of it all, acting as the ‘brain’ that orchestrates how your large language model (LLM)-powered projects talk to databases, cloud services, APIs, design tools and messaging platforms.  This hyperconnectivity makes rapid integration possible. We can now spin up

A deep dive into scaling GitOps for hybrid and multi-cloud environments, exploring architecture, governance, security, observability and real-world enterprise practices.

Chronosphere this week previewed artificial intelligence (AI) capabilities that are embedded into its observability platform that, in addition to helping identify the root cause of an issue, also provides remediation suggestions. Additionally, the company made available a Model Context Protocol (MCP) Server through which AI coding tools and agents will be able to query observability

Discover how AI and large language models (LLMs) are revolutionizing performance testing—shifting from reactive load testing to predictive, continuous assurance powered by intelligent agents and automation.

Discover how context-aware reliability contracts (CARC) redefine SLOs for hyperscale systems—optimizing uptime, reducing infrastructure spend by 33%, and aligning reliability with business value across user tiers, regions, and workloads.

In the past few years, systems have become more complex than ever. Microservices, Kubernetes, cloud environments and distributed application programming interfaces (APIs) have changed how we build and manage software. However, this complexity has also made it harder to find the root cause when things go wrong.  That’s where observability and artificial intelligence (AI) come together to change the game — helping us move from reactive monitoring to predictive root cause analysis (RCA). 

JFrog introduces AI-Generated Code Detection and Shadow AI Detection tools to identify AI-created code, track model usage, and enhance DevSecOps governance across software supply chains.

A survey of 300 enterprise IT and technology leaders published this week finds that while 85% work for organizations that have completed DevOps platform migrations in the last two years, only a quarter (25%) said consolidation delivered expected value within a year. Conducted by the market research firm TrendCandy on behalf of CloudBees, the survey

Bindplane introduces an ability to streamline large-scale OpenTelemetry collector deployments, enabling teams to reuse telemetry pipelines, and improve observability.