Systems | Development | Analytics | API | Testing

Building AI agents is the first step, and it’s positive to see enterprises exploring this avenue. But it’s only the first step. For true enterprise value, these agents must seamlessly connect to your data ecosystem through robust integration, standardized protocols, and be guided by knowledgeable data teams. The need to give AI agents access to data and connect them to the necessary tools and functions has led to the creation of the Model Context Protocol (MCP).

Today, we're excited to officially release the Asgardeo MCP Server, enabling developers to securely manage their Asgardeo organizations using natural language—right from their favorite code editors like VS Code, Claude Desktop, Cursor, Windsurf, and other MCP-compatible clients. Asgardeo already supports Login Flow AI and Branding AI, making it easier to build secure, customized login and registration experiences using plain language.

The future of data and analytics will be nothing like the experience we're used to today. We are at the beginning of a transformation that will fundamentally reshape how businesses use data, make decisions, and create value. At the center of this revolution is Agentic AI. Agentic AI fundamentally changes the way we work with data – moving from passive, reactive AI systems to autonomous, goal-oriented agents capable of reasoning, planning, and executing complex tasks across diverse data landscapes.

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.

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.

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.