E2E testing is broken when written manually. By leveraging real user behavior, production incidents, and AI agents, emergent testing can deliver faster releases, lower maintenance, and better coverage.
4–5 minutes
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July 31st, 2025
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Format
End-to-end (E2E) testing is broken. Not because it’s unnecessary — but because it’s still being written manually. In a world where software evolves faster than test scripts can keep up, trying to hardcode every user journey is a losing game.
What if your E2E tests could emerge dynamically from the actual behavior of your application?
For years, QA teams have been told that E2E tests are essential for catching regressions in real user flows. And they are — in theory. But here’s the problem:
The result? A fragile test suite that breaks often and helps little. Writing robust E2E tests is like trying to freeze the ocean — no matter how hard you try, it slips through your fingers.
Traditional E2E tests suffer from fundamental flaws that make them brittle by design. These tests depend on countless moving parts — databases, APIs, third-party services, UI elements, network conditions, and timing. Any change in the system can cause failures, often for reasons unrelated to actual bugs.
The promise of test automation was speed and stability. But with E2E, that promise has largely failed:
You don’t ship faster — you ship slower, under the illusion of control.
As your application evolves, E2E tests require constant updates. Teams spend more time maintaining tests than improving the product. The ROI becomes questionable when developers dedicate entire sprints to just keeping the test suite green. Passing E2E tests don’t guarantee a bug-free experience — only that specific scenarios work under narrowly defined conditions.
Instead of forcing E2E tests into existence, we should let them emerge from three key sources:
Real users don’t follow the perfect “happy paths” imagined by QA teams. They take shortcuts, make mistakes, use features in unexpected ways, and uncover edge cases no test ever predicted. By analyzing actual behavior through session recordings, analytics, and feedback, we can identify the paths that truly matter.
Every production bug is a story about a missing test. When something breaks in production, it reveals a gap in your QA strategy. Let real incidents guide your testing — not speculative scenarios.
Wherever developers consistently encounter integration issues, you’re looking at high-friction workflows. These are ideal candidates for focused E2E validation — either scripted or agent-based.
Enter agentic AI!
At Thunders, we’ve reimagined testing for the age of intelligent systems. Instead of asking QA teams to write brittle test scripts, we deploy AI agents that:
These agents don’t run static scripts. They generate, evolve, and self-repair test flows in real time. The result? Tests that reflect what users actually do — not what teams assume they do.
This combines the best of emergent testing with cutting-edge AI: learning from usage, adapting to change, and continuously validating what matters most.
In production, agentic testing has delivered:
One client discovered a critical user flow that their QA team had never captured — surfaced entirely by one of our agents.
To shift toward emergent E2E testing:
And above all, move from the mindset of “comprehensive coverage” to “strategic, adaptive coverage”.
The industry has spent years trying to make test automation easier to write. We believe that’s the wrong goal.
The future isn’t about writing better tests — it’s about not writing them at all.
When tests emerge based on real risk and usage patterns:
Let tests emerge — through intelligent systems that learn, adapt, and safeguard your product at scale.
Your users and your team will thank you.
Stop writing E2E tests. Start scaling your testing through intelligence.
→ Want to see how it works in practice? Book a demo
Whether you're getting started or scaling advanced workflows, here are the answers to the most common questions we hear from QA, DevOps, and product teams.
