Resources
Playbooks, guides, and best practices for AI-native E2E testing.
Test-Driven Development in the AI Era
Test-driven development still works when an agent writes your code, but the order of operations changes. Classic TDD assumes a human writes the failing test first; when the agent produces the implementation in seconds, the human can no longer lead with tests. The AI-era loop keeps the test as the source of truth while the agent generates code and verification together, checked against intent, so the regression suite grows as a byproduct of building.
How to Test Apps Built with v0, Lovable, and Bolt
AI app builders generate a working-looking UI in minutes, but nothing in that loop proves the flows behave correctly or keep behaving after the next generation. This guide covers where verification belongs, when to add it, and the workflow for turning generated code into an app with maintained end-to-end tests.
Tests Are the Best Context You Can Give a Coding Agent
A coding agent guesses at what 'working' means unless something tells it. Tests in the repo are that something: they define the target, catch mistakes the moment they happen, and let the agent fix itself before a human ever looks. Here is why tests are the highest-signal context you can hand an agent, and what good agent-readable tests look like.
Verification-Driven Development: Building Software Agents Can Prove
Verification-driven development (VDD) is a methodology where every change ships with proof it behaves correctly, and that proof is generated as a byproduct of building, not a separate phase afterward. This guide defines VDD, traces its lineage from shift-left and spec-driven development, contrasts it with test-after and code-review-only workflows, and shows why the agent era makes it necessary.
What Is Spec-Driven Development? A 2026 Guide
Spec-driven development makes the specification, not the code, the source of truth an AI coding agent builds from. This guide covers the specify-plan-implement-verify loop, how it differs from writing code first, the tools behind it, and where it pays off.
AI Test Automation Pricing: How the Models Work and How to Choose One
A category-level guide to AI test automation pricing: per-seat, usage credits, per-step metering, per-test-under-management, flat managed subscriptions, and quote-only enterprise, plus an honest framework for the cost-versus-hiring question.