Systems | Development | Analytics | API | Testing

Agentic AI systems don’t follow scripts — they make decisions. That means your tests can all “pass” while the AI still hallucinates, misfires, or behaves unpredictably. Traditional QA, built for deterministic workflows, simply isn’t enough. Testing these systems is less like checking a vending machine and more like evaluating a junior employee: you’re judging reasoning, not just output.

AWS MSK has become popular because it deploys Kafka easily and bills alongside other AWS services. Over the past few years, AWS announced Express Brokers, a new cluster type that offers unlimited storage and separates brokers from storage resources. This simplifies scaling and reduces the time needed to rebalance topics when adding or removing brokers.

Real-time AI is transforming how businesses process and use data, demanding faster, more reliable, and scalable infrastructure. Unlike older batch processing systems, real-time AI provides instant insights for applications like fraud detection, personalized recommendations, supply chain adjustments, and predictive maintenance. However, scaling these systems introduces challenges like managing massive data streams, ensuring low latency, and maintaining security.

It’s been almost a year since we released our Konnect Terraform provider. In that time we’ve seen over 300,000 installs, have 1.7 times as many resources available, and have expanded the provider to include data sources to enable federated management of your Konnect organization. There have been many changes in the last year, but there are some changes that we’ve been holding off on as they would break your CI/CD pipelines.

Data streaming equips modern organizations to rapidly ingest and understand new information and use it to solve real-world problems at scale. For some of these real-time insights—critical operational cues that demand a timely response—delivering that information directly to your team’s inbox is the best way to act on it.

Think about launching a new AI chatbot for the company. After a short period, it is providing customers with inaccurate information about your return policy. Within hours, you receive customer complaints, and the customers are annoyed. Your support team is trying to address the technology-induced chaos caused by the AI chatbot. This is happening far more often than you might think, simply because a large number of businesses skip a proper chatbot evaluation platform before deploying their bot.

ATLANTA, GA – August 21, 2025 - Katalon, the AI-native testing company redefining how software teams deliver quality at scale, has been named a Leader in G2’s newly launched AI Software testing category. The recognition affirms Katalon’s position as the strategic partner for global enterprises under pressure to release faster, reduce risk, and deliver reliable digital experiences in the AI era.

Traditional test coverage focuses on code paths and user flows but agentic AI doesn’t follow flows. It reasons, adapts, and improvises. That means your 95% coverage report might look solid while the system still makes unsafe, biased, or unexpected decisions. To test these systems, coverage must evolve: you now measure things like goal alignment, reasoning paths, tool usage patterns, memory accuracy, and failure behavior.

If you are a developer that is running Cursor as your IDE, you have probably had the ability to experiment with different AI agents in pursuit of productivity. One of the most exciting new offerings is DeepSeek V3 is open-source LLM, with added capabilities for code generation, reasoning, and multi-turn conversations.

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