Resources
Playbooks, guides, and best practices for AI-native E2E testing.
Shift Left Testing: What It Means When Agents Write the Code
Moving testing earlier was always the right idea and mostly failed on economics: writing tests early cost developer time nobody had. When the agent writing the feature can also verify it, that constraint changes.
Smoke Testing: The Check That Should Never Be Slow
A smoke test answers one question: is this build broken badly enough that nothing else is worth running? Its whole value is speed, and the usual failure is letting it grow until nobody wants to wait for it.
Subagents: When Splitting the Context Actually Helps
A subagent is a separate context window doing isolated work. It solves a specific problem, context pollution on long tasks, and it is regularly reached for when a simpler mechanism would have been better.
Test Data Management: The Unglamorous Reason Suites Fail
Most flaky end-to-end tests are not timing problems. They are data problems: tests that assume a record exists, or leave one behind that breaks the next run.
What Is API Testing? And What It Cannot Tell You
API tests are fast, stable and cheap to maintain, which is why teams over-invest in them. A fully green API suite is compatible with a completely broken product.
What Is MCP? The Model Context Protocol Explained
MCP is an open standard that lets AI agents call external tools through one interface instead of a bespoke integration per tool. Here is what it actually is, what an MCP server does, where it helps, and what it does not solve.