Systems | Development | Analytics | API | Testing

AI systems are only as reliable as the testing behind them. Red teaming brings a fresh, proactive approach to testing by helping you spot risks early. Hemraj Bedassee , Senior Solutions Manager, AI Testing,

When was the last time your AI assistant confused a memo from the CEO with 'Game of Thrones' plot lines? Have you ever asked a chatbot a question, only to receive an answer that was not only wrong but hilariously outdated? Imagine asking about the latest iPhone model and getting a response detailing the iPhone 3G. We've all had moments where technology gives us a chuckle—until it's crunch time.

BigQuery’s evolution as an autonomous data-to-AI platform relies on new features and services to automate and optimize data analytics and management.

Google just released A2A (Agent2Agent)—a new open protocol for agent to agent communication—and it left people wondering if they just started a protocol war against MCP (Model Context Protocol).

With the release of WSO2 Identity Server 7.1, WSO2 continues to lead with innovation in the open-source identity space by introducing AI-powered development capabilities. The new features introduced, enable developers to secure applications more efficiently using natural language instructions, reducing time, effort, and the required identity domain expertise.

In the prior blog, we previewed some of the reasons that we settled on using Anthropic’s Computer Using Agent (CUA) over other alternatives and promised to provide more information as to what and why, with facts and figures. If you haven’t read that blog, check it out here. In this blog, I hope we can bring to light some of the “why” behind our decisions, and what makes our agentic AI so powerful.

Migrating to BigQuery, the data to AI platform from Google Cloud, just got simpler, with AI-powered BigQuery Migration Services enhancements.