Systems | Development | Analytics | API | Testing

The future of testing isn’t about replacing humans with AI. It’s about augmenting your team’s capabilities. Assistive AI tools can summarize logs, generate test cases, triage defects, and surface insights - all while keeping humans in control. This low-risk, high-leverage approach helps enterprise teams move faster, improve coverage, and focus human judgment where it matters most. Start small, measure impact, and treat AI as a test assistant - not a magic box.

By Erez Schnaider, Technical Product Marketing Manager, ClearML The number of AI applications are rapidly increasing, and it can be difficult to keep up. Every month brings a new protocol, LLM, or tool. In this environment, the true strength of a platform is measured not only by its core features but also by its extensibility and adaptability to change. Many platforms address this challenge by hosting OSS tools or exposing API connections.

With recent performance improvements to BigQuery, users can expect gains in scalability, reliability, and usability across BigQuery and BigQuery ML.

As artificial intelligence (AI) matures from experimentation into production use cases, the symbiotic relationship between data and AI becomes increasingly clear. To deliver real business impact—smarter automation, better customer experiences, and massive cost takeout—AI use cases are only as powerful as the data they’re running on.

Managing a large suite of automated tests, especially across multiple tools, can be overwhelming. The SmartBear MCP (Model Context Protocol) Server centralizes orchestration, monitoring, and prioritization so you can keep pipelines fast, reliable, and easy to manage. The demo video below shows MCP in action, and the sections that follow explain how each capability can help you get more out of both open-source and commercial testing tools. Explore these docs to learn how to get started.

The promise of agentic AI is huge. But how is it impacting the enterprise and the developers and IT professionals most likely to be working with it right now? To find out, Kong evaluated labor market data and surveyed 550 tech leaders, developers, IT decision-makers, and Kong users.

In addition to its existing text embedding models, you can now use both Google’s Gemini and open-source (OSS) models directly within BigQuery.