News and Tech Update

Komodor today extended the reach of its orchestration framework for artificial intelligence (AI) agents by adding support for Model Context Protocol (MCP) servers and the OpenAPI specification. Company CTO Itiel Shwartz said those capabilities will make it possible for IT teams to more broadly orchestrate AI agents that are being used to investigate and remediate issues affecting IT infrastructure. Komodor has already developed more than 50 AI agents that automate the management of Kubernetes clusters running cloud-native applications. By adding support for MCP and OpenAPI, that orchestration framework can now be used to manage hybrid IT environments running more complex applications, said Shwartz. For example, IT teams can use the Komodor orchestration framework to invoke third-party AI agents found on the Komodor Marketplace that have been trained to automate network and storage management tasks or provision graphical processor units (GPUs), he noted. Alternatively, the orches...

Policy as code is usually framed as a compliance tool. It blocks insecure configurations, enforces internal standards, and helps teams prove they meet audit or regulatory requirements. That framing is accurate, but incomplete. The same mechanism can also reduce waste. In many organizations, cloud cost is still reviewed after resources are live and spend is already visible on the bill. By then, the expensive decision has already been made. Policy as code gives platform teams a way to shape those decisions earlier, before waste becomes part of the default path. Why Cost Problems Grow Quietly Cloud overspend rarely comes from one spectacular mistake. More often, it grows through small, routine decisions: Dev environments left running over the weekend Instance sizes chosen for peak demand and never revisited Snapshots, volumes, and logs retained long after anyone needs them Kubernetes requests increased “just in case” Premium managed services used for workloads that are usefu...

Harness today added an ability to automatically secure code as it is being written by an artificial intelligence (AI) coding tool in addition to adding a module to its DevOps platform that discovers, tests, and protects AI components within applications. Secure AI Coding is an extension of the static application security testing (SAST) and software composition analysis (SCA) capabilities that Harness already provides. Additionally, Secure AI Coding leverages a Code Property Graph (CPG) developed by Harness to trace how data flows through the entire application to surface complex vulnerabilities such as injection flaws and insecure data handling. The AI Security module, meanwhile, discovers every call to a large language model (LLM), Model Context Protocol (MCP) server or AI agent that is being made over an application programming interface (API). At the same time, Harness today also revealed it has partnered with Wipro Ltd . to help organizations accelerate AI-native software del...

Sauce Labs today made generally available an artificial intelligence (AI) agent that translates a natural language intent into a set of executable test suites that can run anywhere. Company CEO Dr. Prince Kohli said the Sauce AI for Test Authoring agent closes a gap that has emerged between the rate at which code is being written in the age of AI and the ability of application developers and software engineering teams to validate it. Testing has now become a major bottleneck that is preventing DevOps teams from realizing many of the promises of AI coding, he added. In the absence of an ability to effectively test higher volumes of code, there are now also more applications than ever that have limited test coverage, noted Kohli. In general, Sauce Labs research suggests that even prior to the rise of AI coding, automated test coverage for complex journeys typically plateaus at under 35%. Trained using 8.7 billion real-world test runs to enable 41% faster root-cause analysis than a g...

Oracle released Java 26 on March 17, 2026, and while every six-month release comes with its own set of improvements, this one carries a broader message: Java isn’t just keeping pace with the AI era — it’s actively positioning itself as the infrastructure layer where AI workloads will run. For DevOps teams managing large Java estates, that’s worth paying attention to. The Scale of What You’re Already Running Before getting into what’s new, it helps to remember what’s already in place. According to a 2025 VDC study, Java is the number one language for overall enterprise use and for cloud-native deployments. There are 73 billion active JVMs running today, with 51 billion of those in the cloud. That scale matters when you’re thinking about where AI fits in. Most of the systems where agentic AI will eventually operate — transactional platforms, backend services, data pipelines — are already running on Java. The question for DevOps teams isn’t whether to adopt Java for AI. It’s how to ...

The pattern across AI coding tools this week has been clear: the industry is building governance, review, and safety mechanisms as fast as it’s building capabilities. Google’s latest contribution is plan mode for Gemini CLI, announced March 11 , and now enabled by default for all users. Plan mode puts Gemini CLI in a read-only state where the agent can navigate your codebase, search for patterns, read documentation, and map dependencies — but it cannot modify any files except its own internal plans. The agent researches your request, asks clarifying questions, and proposes a strategy for your review before any code changes are made. The idea is simple: Think before you act. The implementation has some features that make it more interesting than it sounds. How it Works Enter plan mode by typing /plan , pressing Shift+Tab, or asking the agent to “start a plan for” whatever you need. Gemini CLI restricts itself to read-only tools — read_file , grep_search , glob — and can use s...