What We Learned Launching Bugster: How Testing Agents Actually Behave
After shipping Bugster on Product Hunt, we opened signups and saw over 200 companies and teams jump in within 48 hours to assess if Bugster could solve their testing pains. Here’s what the real-world adoption taught us about agent behavior, prompt design, and what it takes to ship a reliable LLM-driven product for developer teams.
Context: What is Bugster?
Bugster acts as an end-to-end testing agent. It runs in the browser, navigating your app exactly like a real user would, and checks that your critical user flows work as intended—no more hand-coding flows, just guide the agent and let it do the rest.
Key Takeaways from Early Usage
Prompt Engineering is Everything
Obvious but underappreciated: “garbage in, garbage out” applies to LLM-native tools just as much as classic ML. The depth and clarity of user prompts drive quality. Vague prompts yield vague outputs—users quickly realize this and dive into our docs for guidance.Agent “Thoughts” Build Trust
Showing the agent’s reasoning step-by-step provides more confidence than even watching the agent interact live. Chain-of-thought UI isn’t just for LLM chat: it’s vital for any agent that’s making decisions. (See Perplexity, DeepSeek, ChatGPT.)Online Evaluation & Notifications are Critical
You need to know, in real time, if things aren’t going as expected. Our combo of Langfuse+Deepeval powers notifications and instant feedback on agent behavior.ETA is a Must-Have
Agents sometimes take minutes, sometimes hours, to complete tasks. Without an estimated completion time, users get frustrated and drop off. Lesson: always show an ETA for long-running tasks.
What’s Unique to Testing Agents?
Every App is Different
Agent performance varies wildly based on your app. Some teams see immediate wins, others hit edge cases. Predicting fit and surfacing likely agent “compatibility” remains an open challenge.Solving the Cold Start Problem
Users often don’t know where to begin. We built onboarding flows that recommend what to test first, using usage data—critical to help teams see value quickly.Single Goal, Single Outcome
The agent should focus on one outcome at a time, not try to solve everything at once.Endless Use Cases, Infinite Tools
The range of testing tasks is vast—we’re already planning to build 100+ specialized “tools” for Bugster’s agent to execute in the future.
Building and launching LLM agents in the wild isn’t just about prompt accuracy or infra. It’s about guidance, transparency, feedback loops, and narrowing the agent’s focus. In testing, especially, these elements determine whether a team feels safe letting an agent touch their most valuable workflows.