Will Zero-UI engineering change the role of testers — from testing screens and buttons to testing agent behaviors?
How do you determine which APIs and event streams are most critical to expose for agent-first interactions?
How does the AX work around the reCaptcha kinda tools?
What are the use cases for QA to start using AI as per your suggestions and experience which works effectively?
Are AX best practices still evolving, or is the field new enough that that they are still being refined?
What are the long-term implications of empowering AI agents with the ability to learn and evolve their own behaviors, and does that mean the AX practice is evolving with them?
If agents are the primary “users,” how do we design trust and transparency for the humans who oversee them?
would you recommend pivoting towards SLMs for efficient model training in context of your org?
Hello All!
When it comes to letting AI agents take the wheel, especially in high-stakes situations, the key is finding the right balance between autonomy and human oversight. A practical approach is to use human-in-the-loop (HITL) systems, basically, letting AI handle routine tasks on its own, but pausing for human approval when the stakes are higher. Think of it like having a co-pilot: the AI can drive on the highway, but the human takes over for tricky turns.
On top of that, organizations should set up monitoring dashboards, audit trails, and alerts. This way, you can track what the AI is doing, quickly catch mistakes, and still benefit from AI efficiency without losing control. It’s about combining speed and safety, letting AI do the heavy lifting while humans stay in charge where it really matters.
QA can play a big role in making sure we strike the right balance between automation and human oversight. One way to do this is by looking at each issue’s risk and impact. For example, small, low-risk fixes can safely go out automatically, while bigger, high-risk changes should get a human sign-off first. To keep everything safe, QA can also set up quick feedback loops, easy rollback options, and post-deployment checks so we catch any issues early. That way, automation speeds things up without sacrificing quality or trust.
Honestly, I think both will stick around and complement each other. Zero-UI is amazing for hands-free experiences, voice commands, and IoT devices—it makes interactions feel almost seamless. But when it comes to more complex tasks, troubleshooting, or just wanting full control and visibility, traditional interfaces are still super important. So, for the foreseeable future, I see a lot of hybrid systems where Zero-UI handles the simple, intuitive stuff, and traditional UIs step in when you need more depth or precision.
Think of AI as having a super observant QA teammate who never gets tired. It can sift through huge amounts of user interactions, session recordings, and even subtle changes in the UI that a human might completely miss. By spotting unusual patterns or behaviors, things that don’t normally happen, it can highlight potential bugs that standard scripted tests might overlook. Basically, AI helps catch those sneaky, hidden issues before they become a headache for users.
Evolving a monolithic or legacy system into a modular, agent-centric setup isn’t easy, it comes with a fair share of headaches. For one, everything is usually tightly interconnected, so changing one part can unintentionally break others. On top of that, missing or unclear APIs and data trapped in silos make it tricky for agents to access what they need. And, of course, people can be resistant to change, old habits die hard.
The good news is there are practical ways to tackle this. Taking an incremental approach to refactoring, adding service layers to make components talk to each other cleanly, and building a strong orchestration layer to manage agent workflows can make the transition much smoother. It’s all about breaking things down step by step rather than trying to rewrite the whole system at once.
Think of it like teaching an AI to explore your app the way a curious tester would. You’d start by letting the AI try different actions in the system, clicking buttons, entering data, navigating flows, and then watch how the app responds. Every time it discovers a bug or hits an unexpected behavior, the AI learns from that experience and adjusts its approach for next time.
To make it even smarter, you’d feed it historical bug data and coverage maps so it knows which areas of the app have been tested thoroughly and which parts are riskier or overlooked. Over time, it continuously improves, focusing its testing on the spots most likely to hide defects, making your exploratory testing more efficient and effective.
Before you push any agent workflow live in a Zero-UI setup, it’s really important to test it in a safe playground first. Think of it like a sandbox environment where you can play around without breaking anything. You can use mock APIs, replay events, or feed in synthetic data to see how your system behaves. Try to cover all the usual scenarios, plus the tricky edge cases and fallback situations. Keep an eye on key metrics like how often tasks succeed and how well exceptions are handled, these give you a clear sense of whether your workflow is ready for production.
If your company is really cautious about opening up its code, a smart way to get started is to focus on small, low-risk areas first. Pick a workflow that’s isolated and not critical to the business, and show how bots can actually make life easier, like saving time or catching errors. Keep things transparent: use clear audit logs and run everything in a sandbox environment so people can see what’s happening without any risk. Once your team sees the benefits in a safe, controlled way, trust will naturally build, making it easier to expand bot usage over time.
When it comes to measuring trust in Zero-UI systems, it’s not just about speed or accuracy anymore. You also want to look at things like how often the system successfully completes tasks on its own, how well it recovers when something goes wrong, and how clear it is about why it’s making certain decisions. Other useful signals are how easy it is for humans to understand the system’s reasoning and how often people feel the need to step in and override it. Together, these metrics give a much better picture of whether users actually trust the system.
Think of it this way: instead of relying on buttons or screens, your app listens and reacts to what’s happening around it, like voice commands, gestures, or sensor data. To make this work smoothly, you want your system to be event-driven, so every input naturally triggers the right action. Add some context-awareness too, so the app “understands” what the user really means. And don’t forget feedback, letting users know their actions were recognized, maybe through sound, vibration, or visual cues, makes the experience feel predictable and reliable. It’s all about creating a flow where interactions feel natural, almost invisible, but still totally in control.
When you’re dealing with agent-driven experiences where the “thinking” happens behind the scenes, it can feel tricky to make sure things are fair and unbiased. A good approach is to use models that are interpretable, ones where you can actually see why decisions are being made. Regularly audit your models and put them through synthetic test cases to check for bias. Also, log how decisions are made and simulate a variety of user scenarios. This way, you can spot inconsistencies and make sure the system behaves fairly across different types of users.
When you’re using AI-driven testing, think of it as giving the AI a map that includes not just the technical paths, but also the business rules and compliance guidelines that matter most. The AI can then focus on the workflows that really move the needle for your business and automatically highlight any steps that might break compliance or stray from critical processes. In other words, it’s like having a smart assistant that knows both the tech and the business side, so nothing important slips through the cracks.