200 teams signed up for our testing agent in 48 hours. Here's what broke.
We launched Bugster on Product Hunt and 200+ companies signed up in two days. Not to "try AI." To solve a specific, painful problem: end-to-end testing sucks, and everyone knows it.
Bugster is a testing agent. It runs in the browser, navigates your app like a real user, and checks that your critical flows actually work. No hand-coded Selenium scripts. No brittle selectors. Just point the agent at a flow and let it go.
That's the pitch. Here's what actually happened when real teams started using it.
Garbage in, garbage out, but worse than you think
You'd think this is obvious. It is. But living it is different from knowing it.
Users who wrote vague prompts got vague tests. Users who were specific got great tests. The gap between the two was enormous. Not 20% better, more like completely different products. The same agent, the same infrastructure, wildly different results based on how someone described what they wanted tested.
This means prompt design isn't a nice-to-have for agent products. It's the product. If your users can't express what they want clearly, your agent will fail, and they'll blame you, not their prompt.
We ended up investing heavily in docs and onboarding flows that teach people how to talk to the agent. That felt weird at first. Now it feels like the most important thing we built.
Show the agent's thinking, not just its actions
We built a chain-of-thought UI that shows the agent's reasoning at every step. Not because it's trendy. Because watching an agent click through your app without knowing why is terrifying.
Here's what surprised us: users trusted the reasoning view more than the live browser view. They'd rather read "I'm clicking the submit button because the form fields are filled and the validation passed" than watch a cursor move across the screen.
Perplexity, DeepSeek, and ChatGPT all figured this out for chat. It applies even more to agents that are making decisions about your production app. If your agent can't explain itself, users won't let it run unsupervised. Period.
You need real-time evaluation or you're flying blind
Agents fail silently. That's the scariest part of building with LLMs. A test can "pass" while testing the wrong thing entirely. Without real-time evaluation, you don't know until it's too late.
We wired up Langfuse + Deepeval to monitor agent behavior in real time. When something drifts (wrong page, unexpected state, hallucinated element) we catch it and notify the team immediately. This isn't optional infrastructure. It's table stakes.
Always show an ETA
This one's embarrassingly simple. Agents take anywhere from 30 seconds to several minutes to complete a task. Without an estimated completion time, users stare at a loading screen, assume it's broken, and leave.
We added ETAs. Drop-off during test runs fell off a cliff. That's it. That's the lesson.
Every app is a different planet
The hardest part of building a testing agent: every app is different. Components render differently. State machines are unique. Auth flows are snowflakes.
Some teams plugged Bugster in and got immediate value. Others hit edge cases within minutes. Predicting which apps will work smoothly and which won't is still an unsolved problem for us. We're getting better at surfacing compatibility signals early, but we're not there yet.
Cold start is real
Most teams sign up and immediately freeze. "What should I test first?" is a harder question than it sounds when you're staring at a blank canvas.
We built onboarding flows that analyze usage patterns and recommend starting points: your most-visited flows, your highest-risk pages, the paths where bugs would actually hurt. Without this, teams churned before they saw value. With it, time-to-first-test dropped dramatically.
One goal, one agent, one outcome
Early on, we let users chain multiple test objectives together. It was a disaster. The agent tried to do everything, accomplished nothing reliably, and confused everyone.
Now each test run has a single goal. One flow, one expected outcome. It's less impressive on a demo and infinitely more useful in production.
Building an LLM agent that people trust with their production testing isn't about having the best model or the cleverest prompts. It's about transparency, fast feedback loops, and the discipline to keep the agent focused on one thing at a time.
The teams that succeed with Bugster aren't the ones with the most complex test suites. They're the ones who write clear prompts, start small, and let the agent prove itself before expanding scope. Turns out, managing an AI agent isn't that different from managing a new hire.