Systems | Development | Analytics | API | Testing

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.

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.

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).

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

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

Amazon Web Services, or AWS, offers a variety of cloud services ranging from AWS resources such as CDNs and data lakes to cloud computing and transformation services such as compute resources, virtual servers, and dynamic availability zones. For this reason, AWS cloud is one of the most broadly adopted cloud solutions, offering a global network of solutions at generally lower costs compared to on-premises solutions.