AI startup Swa Technology today released a multi-model AI orchestration platform that incorporates models from open source and commercial providers to meet engineers where they are at and cut down on shadow AI. While many organizations try to cut down on shadow IT and AI by offering users limited choices of tools, Swa’s platform provides … continue reading
SmartBear releases BearQ agentic QA system SmartBear today announced the release of BearQ, an agentic QA system that can discover how applications work to ensure their integrity. According to the company, BearQ, a part of SmartBear’s Application Integrity Core product suite, can autonomously explore how applications work and adapt to any changes in code, testing … continue reading
The rapid adoption of AI coding assistants has introduced a new and pressing challenge for the software industry: ensuring the security of AI-generated code. Harness, a software delivery platform provider, is tackling this today with two significant product announcements aimed at securing the entire Software Development Life Cycle (SDLC), from the moment code is written … continue reading
At the JavaOne conference today, Oracle made a series of announcements related to a new Java Verified Portfolio (JVP) and new JDK Enhancement Proposals (JEPs). The JVP is designed to give developers a trusted set of tools and services to support today’s speed of AI-driven application development, and also includes commercial support for the JavaFX … continue reading
Agentic application security company Checkmarx today unveiled Checkmarx One, a platform built for the age of agentic development. The platform embeds agentic, AI-driven security across code, dependencies, AI assets and runtime, which enables enterprises to have oversight and visibility into security right from the start. “Traditional AppSec was never built to deal with AI coding,” … continue reading
The world of AI is rapidly shifting from simply asking intelligent systems questions to delegating real work to autonomous AI agents. However, as these agents proliferate, a critical challenge remains: the lack of secure, isolated infrastructure to run them safely within an enterprise environment. This is the problem being tackled by NanoClaw and Docker, whose … continue reading
CData Software this week announced enhancements to CData Connect AI that bring new capabilities to CData’s managed Model Context Protocol (MCP) platform, adding to connectivity, context, and control — the three pillars required to move AI from experimentation to production. For connectivity, a new Connect Gateway enables AI systems to work with live data from … continue reading
Tricentis Introduces End-to-End Enterprise Agentic Quality Engineering Platform Software QA provider Tricentis has brought out an agentic quality engineering platform powered by the new Tricentis AI Workspace, which works by deploying AI agents to manage risk while enabling rapid innovation. AI has changed the face of software development and deployment in terms of the pace … continue reading
Anthropic today is releasing a preview of Claude Code Review, which uses agents to catch bugs in every pull request. It is in research preview to Team and Enterprise users. With AI tools creating code at a remarkable pace, code reviews are becoming a bottleneck, so most PRs are getting light scans instead of deeper … continue reading
JetBrains today is launching its Air agentic development environment into public preview. Air was created to enable developers to build tools around agents, guide them and fine-tune their outputs. According to the company, Air is designed to end the current state of agent fragmentation, in which each agent runs in a different tool, with different … continue reading
The business world is on the cusp of a profound shift, moving away from the “data-driven” mantra to one that is “decision-centric,” powered by Decision Intelligence Platforms (DIPs). This emerging category, which recently saw its inaugural Magic Quadrant from Gartner signifies that the focus is shifting from simply analyzing data to actively augmenting and automating … continue reading
The rise of AI-infused applications, particularly those leveraging Large Language Models (LLMs), has introduced a major challenge to traditional software testing: non-determinism. Unlike conventional applications that produce fixed, predictable outputs, AI-based systems can generate varied, yet equally correct, responses for the same input. This unpredictability makes ensuring test reliability and stability a daunting task. A … continue reading
