Your UI tests are too predictable. Learn how we combined Playwright with LLMs to find edge cases we never imagined.

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A comprehensive tutorial on building an adversarial UI testing framework that combines Playwright with LLMs to discover edge-case bugs that deterministic test suites miss. The system extracts a token-efficient JSON snapshot of live UI state, sends it to an LLM with a hostile system prompt targeting state corruption, race conditions, and validation bypasses, then executes the generated action plan via Playwright and feeds results back for iterative deepening. The article covers the full implementation in TypeScript including state extraction, snapshot compression, Zod-validated LLM response parsing, anomaly detection logic, and multi-round orchestration with convergence criteria. Real-world results showed 14 unique logic bugs found across three applications with 90%+ test coverage, including a form wizard validation bypass, a shopping cart negative-total bug, and a role-based rendering race condition.

Playwright + LLM: Building an Adversarial UI Logic Tester

How to Build an Adversarial UI Logic Tester with Playwright and LLMs

Architecture Overview: How the Pieces Fit Together

The Adversarial Planner: Prompt Engineering for Hostility

Executing Adversarial Actions with Playwright

Real-World Results: What This Actually Finds

Limitations, Costs, and When Not to Use This

From Predictable Tests to Adversarial Resilience