Code Quality

I wrap up the series with the operational side: how to launch the workflow daily. A persistent plan.md, pre-commit hooks as guardrails, a multi-level CI pipeline, and a simple audit to adopt an existing repo.

I connect the workflow to a modern stack: React on the frontend, Go with Gin, GORM, and PostgreSQL on the backend. Three structural choices keep the suite reliable: testcontainers-go, strict MSW, and Pact.

Example-based tests miss the edges. Property-based testing flips the approach. I show six useful patterns, stateful PBT for sequence bugs, and why this topic is well-suited for AI-assisted use.

A synthesis of Mario Zechner's ideas on coding agents. Why the minimalism of the Pi agent, coupled with strict context engineering and CLI tools, often beats heavy frameworks.

A test suite can show good coverage and miss an obvious bug. To measure real quality, I look at four complementary axes, with a simple audit to apply to an existing project.

Once the spec is in place, I split the work between humans, AI, and deterministic tools. The critical test does not always come from the same author as the code, and the critic-agent often matters more than the writer-agent.

The first step in the AI workflow is specification. This article shows how I turn a vague user story into something a human and an agent can use without guessing, with Gherkin, typed examples, and properties.

The implicit promise of AI coding assistants was that tests would become a thing of the past. The reality documented by Kent Beck, ThoughtWorks, and several 2025 studies is the opposite: with AI, tests become essential — but the work has shifted.