Resources
Playbooks, guides, and best practices for AI-native E2E testing.
Can You Trust AI-Generated Code?
Trust in code has never come from trusting the author. It comes from evidence. So the real question about AI-generated code is not whether the model is good enough, but whether you can prove each change behaves correctly, cheaply enough to do it every time.
How to Catch Hallucinations in AI-Generated Code
AI coding agents invent packages, APIs, and methods that do not exist, and describe UI behavior that never happens. Most of these hallucinations compile and pass review. Here is how to catch each category, including the behavioral ones that only show up when the code runs.
CI/CD for Agent-Written Code
When coding agents open most of your pull requests, unit tests passing is no longer proof the change is safe to merge. This guide covers the pipeline stage AI-heavy PR flows are missing: a PR-time behavioral gate that verifies each change in a real browser before it merges, with GitHub Actions setup notes.
Context Engineering for Coding Agents
Context engineering is the practice of curating exactly what a coding agent sees at the moment it acts: instructions, repository knowledge, tools, tests, and runtime feedback. This guide breaks down the kinds of context, the principles for managing them, and why a checkable definition of correct is the highest-value context you can supply.
Continuous Verification for AI-Generated Code
Continuous integration proved the code compiles. Continuous testing proved the existing tests pass. Continuous verification proves that each change actually behaves the way it was meant to, on every change, at the speed AI coding agents now produce them.
How to Test Code Written by Cursor
Cursor writes and edits UI code fast, but it rarely confirms the result works in a browser. This guide shows how to give Cursor a real browser to verify its own changes and author maintained YAML end-to-end tests that live in your repo.