Systems | Development | Analytics | API | Testing

At this year’s Big Data London, Keboola CEO Pavel Doležal sat down with data expert Christina Stathopoulos on EM360Tech’s Don't Panic, It’s Just Data podcast to explore what’s next in enterprise data automation. The conversation centered on a quiet but powerful shift in how AI, particularly large language models (LLMs), is changing the way organizations interact with their data platforms - not just by querying them, but by acting on them.

We’re excited to announce the general availability of Kong Insomnia 12!

At Bitrise, our goal is to help developers speed up their builds and automate tedious processes, from first code commit all the way to production release. To advance this mission, we began exploring how AI can improve developer workflows. Over recent months, I joined a small, fast-moving tiger team focused on cutting through hype to identify valuable AI capabilities. Our goal: to design, develop, and maintain production-grade AI features that truly help developers.

Across industries, AI systems are being scrutinized under new laws that demand proof of fairness, transparency, and human oversight. Hemraj Bedassee , Delivery Excellence Practitioner,

Traditional API Gateways: Excellent for routing, auth, and microservice traffic; poor at AI workloads. Limitations: Can't track tokens, manage streaming responses, enforce content-level security, or use semantic caching. AI Gateways Purpose-built for LLMs with: Architecture Recommendation: Layered approach: Benefits: Lower costs (20--40%), better performance, centralized governance, future-proof AI infrastructure. Market Context.

If you recall part one of this blog post, we were going to use ChatGPT in parallel with how we would work to cover these aspects of performance testing. We left the first part of this blog post at the point at which we had compared Requirements Gathering and Risk Assessment, we will pick this post up by looking at Script Creation before concluding with Results Analysis. Our performance testing tool of choice will be JMeter.

If you believe many articles online you would believe that automation in testing will soon be defined, managed and executed by Artificial Intelligence (AI). AI is embedded in many organisations technology landscape and to think that this model will change is shortsighted. AI is here to stay undoubtedly in one form or another, but should it be responsible for the automated testing of your applications under test?