Systems | Development | Analytics | API | Testing

Kong AI Gateway 3.13 moves enterprises from AI experimentation to shipping production-grade agents by unlocking new capabilities focused on agentic security, developer productivity, and resilience, including MCP tool-level access control, expanded provider support, and smarter load balancing.

Discover how leading companies use reverse ETL to close the communication gap, unlock data trust, and turn complex data into meaningful business impact.

If your company handles customer payment information, it’s critical for you to understand PCI DSS compliance requirements. A single breach can result in substantial financial penalties and damage your brand's reputation. In my experience working with enterprise customers, I’ve seen firsthand how non-production environments often become a blind spot for compliance efforts.

Testing is changing, and AI is leading the next step Every QA team faces the same pressure: test more, deliver faster, and never miss a defect. But as projects grow and release cycles shorten, running every test in every sprint isn’t always realistic. The challenge isn’t just about automating more, it’s about deciding what to test first. That’s where AI comes in.

Every compliance team knows regulatory change is constant, but 2026 is shaping up to be a perfect storm. With SEC climate rules about to take effect, CSRD deadlines accelerating, and FASB updating requirements every few months, reporting expectations are moving faster than most infrastructures can keep up. You finalized your 10-K template in January. By March, FASB changed two requirements. Now you’re rebuilding everything again.

Talk to anyone who works with data, and you’ll hear a familiar story: Data engineers are still bogged down cleaning, prepping, and untangling semantic models. Analysts are churning out dashboard after dashboard, with little time left for real analysis. Developers are hand-coding embedded analytics, turning every new feature into a months-long project. And business users are stuck in line, waiting for answers.

In an age where software developers are speeding up their code development to meet the demand of rapid application deployment, there are new tools being developed based on Artificial Intelligence (AI) technology. Replit and Cursor have received a lot of excitement for both of these platforms due to their use of artificial intelligence in assisting developers with coding.

Every business running Dynamics 365 Finance and Operations (D365 F&O) relies on the promise of “Evergreen Updates,” which provide seamless, continuous improvement with zero disruption. For a major hospital which was already stretched thin by rising costs and patient loads, the latest update was supposed to be a lifeline, delivering new financial automation features.

Engineering teams want to understand the real health of their applications – not just what was planned or what was tested, but what is actually happening in production. The challenge is that these signals live in different systems, each optimized for a specific part of the delivery lifecycle. Test execution data, issue tracking, and production monitoring each describe a different aspect of system behavior. On their own, they answer narrow questions about validation, delivery, or stability.

RAG evaluation measures how effectively a system retrieves relevant context and uses it to generate grounded answers. These evaluations detect hallucinations, measure retrieval precision and reveal where pipelines degrade after model updates or knowledge-base changes. Engineers rely on these tools to maintain output quality, prevent regressions, validate prompt and architecture choices and ensure that production answers stay aligned with trusted sources.