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Tricentis Preps Wave of Additional AI Testing Capabilities

Tricentis is providing early access to multiple artificial intelligence (AI) capabilities that it is gearing up to roll out later this year via a Tricentis Transform initiative, including an autonomous AI agent, dubbed Aida, that explores web and Windows desktop applications to surface defects, weaknesses and other potential gaps without requiring DevSecOps teams to create scripts. Additionally, Tricentis is developing a Release Risk Intelligence capability that surfaces release-scoped coverage gaps, prioritizes risks by severity, and launches AI tasks to resolve issues that are informed by context provided by the automated Tricentis testing platform. Finally, Tricentis is also readying AgentScore, a tool that enables organizations to evaluate AI agents by observing how agents behave in real-world workflows, recommending what should be measured, and generating composite quality scores that include review, block, or ship recommendations. David Colwell, vice president of AI and mach...
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Is Java Enterprise Ready for AI? Absolutely

AI is transforming software engineering. For enterprise Java developers, the key question is whether Java and Jakarta EE are prepared to integrate AI into enterprise applications. The answer is yes . Java and Jakarta EE already support integration of Large Language Models (LLMs) and AI capabilities through existing APIs, libraries, and frameworks. Developers can continue using the enterprise Java ecosystem without waiting for new specifications. Meanwhile, ongoing initiatives are working to standardize AI programming models within Jakarta EE. AI and Software Engineering AI is changing both how developers build software and what applications can do. AI supports development through code generation, review, testing, documentation, and specification-driven tasks. In applications, it classifies information, generates content, summarizes data, assists users, and participates in business workflows. As AI becomes more autonomous, its architectural impact grows. Applications may use AI as ...
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Why DNS, DHCP, and IPAM Can No Longer Live in Separate Silos

Ask a network engineer where their DNS records live, and the answer is rarely straightforward. Some zones reside on Windows DNS servers, others are hosted in cloud-managed services, while DHCP scopes are spread across regional data centers, branch offices, and virtual networks. Meanwhile, IP address visibility is frequently fragmented across different management platforms, making routine tasks such as provisioning, troubleshooting, and auditing more time-consuming than they should be. This fragmentation isn’t new, but its impact has grown significantly. As organizations adopt hybrid and multi-cloud architectures, every infrastructure change depends on DNS, DHCP, and IP address management (IPAM), collectively known as DDI, working together. Yet these services are still commonly managed through separate tools, disconnected workflows, and isolated teams. This results in delayed DNS updates, inconsistent DHCP configurations, stale IP records, and limited visibility across environmen...
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The AI Agent Race Is On. But Are We Watching the Right Race?

Claude Code, OpenAI Codex, GitHub Copilot, Cursor and a growing field of challengers are competing to define the future of software development. A new Techstrong special report examines who is ahead, how the race should be measured and what enterprises need to consider before placing their bets. Which AI coding agent is winning? It sounds like a simple enough question. Claude Code has surged among professional developers. OpenAI Codex is gaining ground quickly. GitHub Copilot enjoys enormous distribution and sits inside the repository and pull request workflows enterprises already use. Cursor has emerged as an agent-native development environment. Google brings powerful models, cloud infrastructure and developer reach. Devin and other specialists are pursuing longer-running autonomous work, while open source projects such as OpenHands, Cline, OpenCode and Aider give organizations more control over models, data and deployment. There is no shortage of contenders. There is also no shor...
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Report Shines Spotlight on 91 Vulnerabilities Fixed in Latest Update to Spring Framework

A report published by Sonatype identifies more than 91 vulnerabilities that have been remediated in the latest update to the open source Spring framework for deploying Java applications mashed available by Broadcom earlier this month. Released earlier this month, the 91 vulnerabilities affect 209,569 software components that will need to be updated. Sonatype CTO Brian Fox said this large number of vulnerabilities that are being simultaneously released is another indication the providers of major software platforms are racing to pay down massive amounts of technical debt before vulnerabilities are discovered and exploited by cybercriminals that are gaining access to advanced artificial intelligence (AI) models. Providers of platforms such as Spring already have access to those same AI models, which has given them a head start to find and remediate vulnerabilities before adversaries exploit them. In fact, Broadcom between March and April increased the number of advisories it has issu...
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Why “Tokenmaxxing” Was Always the Wrong Way for Developers to Measure AI Productivity

The term “tokenmaxxing” left the developer lexicon just as quickly as it arrived, and like most viral technology concepts, it means different things depending on who’s using it. In practice, the term is being applied in two ways: maximizing total token consumption as a proxy for AI adoption and effort, or optimizing output per token as a measure of efficiency and skill. These are two very different things, and conflating them is causing organizations to reach for the wrong measurement framework at the wrong time. Why might this be happening? Token usage is a function of query volume and context: how many prompts are sent and how much information is loaded into and out of a model with each exchange. That means token consumption reflects how actively a developer is engaging with AI tooling. In the early stages of adoption, that signal has value: it can tell you whether someone is using AI at all, but unfortunately it cannot tell you whether that usage is producing anyth...
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Preparing Infrastructure for the Next Phase of Agentic AI

After years of exploring how AI can support agency operations and make better use of growing volumes of data, agencies are now preparing for the next phase: agentic AI. The infrastructure needed is changing along with it. Agentic AI, unlike regular AI capabilities, can coordinate multiple specialized agents to carry out complex, multistep workflows, which means agencies are no longer planning only for the compute required to run AI models. They also have to account for the orchestration between agents, the movement of data across environments, and the points where human judgment still belongs. That makes it important to start with the work itself—not the newest, shiniest technology. The following three strategies can help. Start With the Workflow, Not the Hardware Agentic AI is more sophisticated, but it also requires more orchestration and preparation. Consider a permitting process. One agent might evaluate municipal requirements while another examines state or federal statutes. A...
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