Ah, this one’s pretty interesting! Basically, if you want to quickly see how AI agents hold up against real-world threats, think of it like “stress-testing” them safely. Teams usually do this by setting up sandbox environments, kind of like a safe playground where AI can get tested without breaking anything in the real world.
They also use adversarial testing tools, which basically throw tricky, unexpected scenarios at the AI to see how it reacts. And to take it a step further, some teams use cloud-based red-team simulators tools like Chaos or Threat-to-Test, that mimic real attacks on a bigger scale.
So, in simple terms: give the AI a safe place to mess up, throw challenges at it, and see how it handles things before it faces the real world. Makes testing much faster and safer!
Honestly, the best way to check if you’ve shipped the right product without burning a ton of money is to keep it lean and test early. Instead of building the full thing from day one, you can try things like usability tests to see how real users interact with it, or shadow launches, releasing it quietly to a small audience to gather feedback. Keep a feedback loop going so you learn what’s working and what’s not, and always make sure what you’re building lines up with your business goals or key metrics. Basically, it’s about learning fast, iterating, and making sure you’re solving the right problem before going all in.
Oh, this one’s interesting! So, if we’re talking about Claude Agents, Cline, and Cursor, each of them brings something a bit different to the table:
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Claude Agents are like your super-smart code buddy. They’re amazing at reasoning and understanding your code in natural language, so when you’re doing code reviews, they can really help catch issues or suggest improvements.
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Cline is more focused on automation for testing. Think of it as a tool that helps you run tests faster and more reliably without manually checking everything yourself.
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Cursor is all about boosting developer productivity. It’s like having a helping hand for coding, making repetitive tasks easier, suggesting snippets, or just speeding up your workflow.
So basically, Claude is your brainy reviewer, Cline is your testing sidekick, and Cursor is your coding assistant. Each shines in its own way depending on what you need at the moment.
Yes, it can automatically create an MCP to connect to an API, but there’s a bit of setup involved. You’ll need to do some schema mapping and set up guardrails to make sure everything works as expected. Autogeneration is definitely possible, but you still want to double-check it for correctness and security before using it in production.
Ah, this is a great question! So, once you’ve connected Jira MCP to your LLM, the key is to set the right context first. Think of it like giving the model all the background info it needs, like the ticket details, requirements, and acceptance criteria.
Once that context is in place, you can prompt the agent to generate test cases that actually make sense for your workflow. You can also guide it by mentioning the risk level for each feature or task, so the generated tests are both relevant and meaningful.
Basically, it’s about feeding the right info upfront and then letting the model tailor the test cases to fit your project’s needs. Makes the whole process way smoother!
Hey All! How’s it going?
What did you think of the Testμ 2025 session on “Ship Code. Without Writing It”?
I found it really eye-opening.
When it comes to building trust in AI-written code before putting it into production, companies usually take a step-by-step approach rather than just deploying it straight away. They start with peer reviews, so real developers double-check what the AI generated. Then, they run static analysis tools to catch potential issues automatically.
Next up is making sure the code is reliable and reproducible, what we call reproducible builds, and running regression tests to ensure existing functionality isn’t broken. Finally, instead of going all-in at once, they do staged rollouts while closely monitoring performance to catch any surprises early.
So essentially, even if AI writes the code, there are multiple safety nets in place to make sure it’s solid before it reaches production.
Hey everyone!
How are you?
What did you think of the session? I found it super insightful. 
One thing that really stood out to me was how agentic AI can totally change the way testers work. Here’s what it brings to the table in a simple way:
- Automated exploratory testing: The AI can explore your app on its own and spot bugs that we might easily miss.
- Risk prediction and prioritization: It helps figure out which parts of the app are most likely to have issues, so you know what to test first.
- Context-aware test generation: It understands the app’s context and creates smarter, more relevant tests automatically.
- Faster security and compliance checks: Tasks that usually take a lot of time, like security and compliance checks, can be done much quicker.
Basically, it’s like having a super-efficient testing buddy that thinks ahead and saves you a ton of effort.