Systems | Development | Analytics | API | Testing

If you’re a current customer of Informatica, it may be time to consider alternatives.

If you work in QA or have experience in software engineering, it’s likely that you’ve heard the terms “test plan” and “test strategy” used interchangeably. In actuality, they’re quite different, and understanding those differences will help you write clearer documentation, run a tighter team, and ship higher-quality software. This guide breaks down test plan vs test strategy in practical terms, then shows how they fit together on real projects. Ready?

Zero-trust security ensures you verify every interaction, whether it’s a user, system, or API, before granting access. For large language models (LLMs), this approach is vital to prevent data breaches and maintain control over sensitive information. Here’s how zero-trust principles apply to LLMs: Identity Verification: Use multi-factor authentication (MFA) for users and secure API keys for systems. Regularly review and update permissions.

Agentic AI systems don’t behave the same way twice, so traditional test cases with fixed inputs and expected outputs no longer work. But unpredictability doesn’t mean untestability. Instead of checking for exact answers, testers must probe for unsafe, misaligned, or unintended behavior. Techniques like scenario replay, adversarial prompting, constraint injection, and behavioral thresholds help uncover risk, drift, and reasoning errors.

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.