Systems | Development | Analytics | API | Testing

Global enterprises HubSpot, Saks, DocuSign, and Oldcastle Infrastructure modernized their data infrastructure on Fivetran and Snowflake, achieving AI-driven innovation, scalability, and millions in ROI.

AI is transforming software testing by introducing intelligent automation techniques. Unlike traditional scripts that follow static instructions, AI-driven testing uses machine learning, computer vision, and NLP to adapt and make data-driven decisions during testing. This shift offers significant advantages. AI can rapidly analyze large datasets (requirements, code changes, past failures) to identify high-risk areas and prioritize testing efforts.

During our recent webinar Quality Horizon 2025, the virtual room buzzed with energy, filled with insightful questions that pushed our thinking forward. But one particular query truly struck a chord, a question that elegantly highlighted a core challenge in AI-driven testing: The observation was spot on. It perfectly captured a critical limitation we’ve seen across the current AI testing landscape.

This article originally appeared on BigDataWire on Feb. 26, 2025. Artificial intelligence (AI) agents are set to transform enterprise operations with autonomous problem-solving, adaptive workflows, and scalability. But the real challenge isn’t building better models. Agents need access to data and tools as well as the ability to share information across systems, with their outputs available for use by multiple services—including other agents.

Why do one in three data professionals cite integration challenges as the biggest roadblock to AI success? Our latest research uncovers the missing link to AI-ready data.

The hype for Anthropic’s Model Context Protocol (MCP) has reached a boiling point. Everyone (including Kong) is releasing something around MCP to ensure they aren't seen as falling behind in the ever-changing AI landscape. However, in this mad dash, there remains confusion around MCP and what this standard actually enables. Some see MCP as a total game-changer, and some see it as little more than a thin and unnecessary wrapper. As usual, the truth lies somewhere in between.

In 2025, the integration of Artificial Intelligence (AI) into Extract, Transform, Load (ETL) processes is transforming the data engineering landscape. Traditional ETL workflows are evolving from rigid, manually scripted pipelines into intelligent, adaptable systems powered by AI. These AI-driven ETL tools enable companies to handle increasing data complexity, schema drift, and real-time transformation demands without massive engineering overhead.

ClearML v3.25 introduces native support for vector databases within the Hyper-Datasets feature. This release enables users to store and search embeddings directly inside ClearML, opening the door to powerful custom RAG pipelines. In addition, v3.25 includes expanded orchestration metrics, new Application Gateway UI, and a range of UI upgrades to streamline day-to-day operations.

The need for impeccable mobile applications is unequivocal. Users want intuitive interfaces, smooth functionality, and uniform performance across various devices and operating systems. Development teams have a considerable difficulty in satisfying these requirements while expediting release cycles. Conventional mobile app testing services, although fundamental, often fail to keep pace with the velocity and complexity of contemporary application development.

Agentic AI has the potential to revolutionize workplace processes in nearly every sector by improving business decision-making, workflow efficiency and customer interactions and experiences. While interest in agentic AI is widespread, the motivation to use it differs by industry. Cloudera surveyed 1,484 enterprise IT leaders across 14 countries to better understand their approach to agentic AI in 2025, including how specific industries plan to implement the technology.