It will automatically create MCP to connect any API?
After connection with Jira MCP to LLM how to create the correct test cases. How we can setup the context and use in MCP protocol ?
How do enterprises build trust in AI-written code before deploying it to production?
What agentic AI can bring for tester on the table?
That’s a really good question, and honestly, it’s something a lot of teams are figuring out right now with AI-driven commits. What’s been working well is combining a few safety nets instead of relying on just one.
For example, start with static analysis tools they’re great for catching obvious red flags early (like potential security or performance issues). On top of that, make sure you’ve got a solid suite of automated unit and regression tests running, so you can quickly see if any new code breaks existing functionality.
And the cool part? There are now AI-assisted commit analyzers that help you spot risky changes before they get merged. They’ll highlight things like security concerns, dependency issues, or performance hits, which saves you from nasty surprises later.
So in short: let AI write code, but let automation (and smart checks) guard the gate before it goes live.
Honestly, no-code tools are awesome for things like quick prototypes, simple workflows, or basic CRUD apps you can get things up and running really fast. But once you move into more complex, large-scale, or very specific projects, you’ll almost always need developers to step in. Think of no-code as a great accelerator, not a full replacement for devs.
Honestly, creating an agent that spawns billions of other agents automatically isn’t really practical right now. It sounds cool in theory, but in reality, it ends up being super inefficient and a nightmare to manage you’d spend more time coordinating all those agents than actually getting work done.
A much smarter approach is to focus on composable, reusable agents basically, smaller agents that each specialize in a specific task. You can mix and match them as needed, which boosts productivity without the chaos of billions of running agents.
Ah, this is a great question! If AI in IDEs starts auto-suggesting test cases, the key is not to blindly trust everything it throws at you. Think of it like having a smart assistant, it can draft a lot of tests, but you still need a human eye to make sure they actually make sense.
A couple of practical ways to keep them meaningful:
- Coverage check: Make sure the suggested tests actually cover the important parts of your code, rather than just creating generic “boilerplate” tests.
- Human review: Go through the AI-generated tests and validate them against the actual requirements and risk priorities. This ensures you’re testing what matters most, not just what the AI thinks is interesting.
In short: let AI do the heavy lifting, but always have humans steer the ship. That combo usually gives you the best, most meaningful test suite.
Think of Agentic AI like an assistant, you give it directions, set boundaries, and it works under your guidance. Autonomous Agentic AI, on the other hand, is more like a self-driving car, it can handle tasks from start to finish and make decisions on its own, without waiting for you to approve each step.
Absolutely! So, Cline is kind of like a smart assistant specifically for testing. It uses AI agents to help generate test cases and even automate them, which makes it a lot more testing-focused. On the other hand, Cursor is more about helping you write code faster and smarter, while Kiro leans towards boosting overall productivity. So, if your main goal is testing, Cline is the one that really shines.
With the rise of tools that let you “ship code without writing it,” some pretty interesting roles are starting to pop up in the software world. For example:
- AI Orchestrator – This is someone who knows how to design prompts and provide the right context so AI can generate code that actually works. Think of it as guiding the AI rather than typing out every line yourself.
- AI QA/Tuning Engineer – Even though AI can generate code, it’s not perfect. These folks test, fine-tune, and make sure the AI outputs are solid and reliable.
- Compliance & Safety Tester – As AI-generated code becomes more common, ensuring it meets legal, ethical, and security standards is huge. These testers make sure nothing slips through the cracks.
For newer developers, the shift isn’t just about learning a programming language anymore. It’s more about evaluation, oversight, and risk management. Basically, understanding how to guide AI, review its work, and make sure everything is safe and compliant. So instead of focusing solely on writing code, start thinking about how you can work with AI effectively.
Hi All! 
When it comes to testing low-code or no-code apps, there are definitely some upsides and a few challenges to keep in mind.
On the plus side, these platforms can really speed up delivery. Since you don’t have to write all the code from scratch, you can get your app ready faster and with less technical overhead. This is especially great if you want to focus more on functionality and less on the nitty-gritty of coding.
On the flip side, there are some things that can make testing a bit tricky. Sometimes the underlying logic of the app is hidden, which means you don’t always have access to all the test hooks you might need. Security checks can be harder too, and there’s always the risk of vendor lock-inbasically, you’re tied to the platform, and switching later can be a headache.
So, while low-code and no-code make building apps faster and easier, testers need to be aware of these limitations and plan accordingly.
Hi Community members!
Hope you all are doing great!
Vibe coding is all about intent-driven, collaborative workflows using low-code and no-code approaches. Testing is usually easier for repetitive tasks, but if your logic is complex or hidden in layers of abstraction, it can get a bit tricky.
Ah, this one’s actually pretty simple if you think about it in terms of “who’s in control.”
- Agentic AI is like having a really smart assistant: it can do tasks for you, follow instructions, and make some decisions—but there’s always a human keeping an eye on it. You’re the one ultimately steering the ship.
- Autonomous agentic AI, on the other hand, takes things a step further. It’s more like giving that assistant its own set of keys and letting it navigate the ship on its own. It can make decisions, plan actions, and adjust on the fly without needing constant human oversight.
So basically, it boils down to oversight vs. self-governance: one still checks in with you, the other charts its own course.
For really specialized areas, think EV charging, fintech, or medical imaging, AI can’t just rely on a generic model. It needs a bit of extra help. That usually means training it with datasets that are specific to the domain, using “domain adapters” to make sense of the unique workflows, and having experts double-check its outputs. In short, it’s about combining AI’s speed with human know-how to get accurate results.
Honestly, the best way to avoid blindly trusting AI suggestions in your IDE is to keep human checks in the loop. Make sure code reviews are a regular thing, track your test coverage metrics, and manually focus on testing the critical paths in your application.
Think of AI as a helpful assistant, great for giving ideas or speeding things up, but it shouldn’t replace your own judgment as a tester. Always question the suggestions and validate whether they actually cover what matters most.
Ah, this one comes up a lot! 
Honestly, it really depends on what you’re mainly doing.
- Cursor is awesome if you’re coding a lot, it’s like having a super helpful pair of hands while you write code.
- Cline, on the other hand, really shines for QA and testing workflows. It’s built to make those processes smoother and faster.
So, if you’re more into development, Cursor might feel like your best buddy. But if QA and testing is your thing, Cline could give you a real edge. It’s all about picking the tool that matches what you spend most of your time on.
Absolutely! Think of it this way: testing AI agents to defend themselves is a lot like training a security guard. You put them through red-team exercises, simulated attacks designed to see where they might slip up. You also throw adversarial prompts at them, which are tricky or misleading inputs, to see how they respond under pressure. On top of that, keeping an eye on their attack surface all the points where they could be vulnerable, is key.
The catch? This isn’t a one-time thing. Just like people, AI agents need continuous training to stay sharp and keep up with new types of threats. So it’s an ongoing cycle of testing, learning, and improving.
If you want guys, I can also give a quick real-world example of how this works in practice, it makes it super relatable. Do you want me to?
Hey All! How’s it going? 
No-code and low-code tools are amazing, but there are some projects where traditional coding still makes more sense.
For example:
- High-performance apps that need every bit of speed.
- Security-heavy or compliance apps like finance or healthcare.
- Highly customized enterprise software where off-the-shelf tools just don’t cut it.
So, if your project needs speed, security, or deep customization, sticking to code is usually the safer bet.
Yes, there are definitely some unique risks when it comes to no-code, low-code, or AI-driven workflows. For example, sometimes the logic behind what’s happening can be hidden, making it harder to spot errors or unintended behavior. Audit trails might not be as transparent as in traditional code, and there’s often a dependency on the platform or vendor you’re using. Because of these factors, these workflows actually need more human oversight, not less, so it’s important to stay involved, review the processes regularly, and make sure everything aligns with compliance and regulatory standards.