Systems | Development | Analytics | API | Testing

Ever since AI tools came into the picture, it has transformed a lot of industries. An industry most evolved due to this revolution of AI is the software Development industry. There have been discussions about AI for coding being so good that it holds the potential to replace developers, which might be debating but precisely a false claim.

AI is no longer just a technical feature, it is a business-critical system that shapes conversations, decisions, and customer experiences.

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.

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?

You will encounter debugging while developing backend systems, web applications, or automation scripts – it’s an inevitable step in the development life cycle. Debugging is a necessary step preceding any delivery of a software product. The Python ecosystem academic is different in that you may not see your errors until you are running the code. Having a stable way to debug your Python code is very important for continuing your development journey.

Amazon S3 became the de facto standard for storing objects due to its cheap price, and it’s designed for high durability, with a 99.999999999% durability guarantee. We can talk a lot about Amazon S3, but today in this blog, let’s see how to upload a file to S3 using the REST API. I hope most of you have tried using the SDK approach with boto3, but today let’s see the different ways to upload a file to S3 using the REST API and guess what, we’ll see a demo as well.

If you’re in QA or a developer who’s been looped into test automation, chances are you’ve heard the term UFT tossed around. Maybe you’ve worked with it back in the day when it was called QTP (QuickTest Professional), or maybe you’re hearing it for the first time amidst all the buzz around Selenium and Cypress. But here’s the thing: UFT (Unified Functional Testing) isn’t some relic of the past.

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.

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.

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.