Building Agentic QA Systems: First Principles

What Is an Agentic QA System?

An agentic QA system is one where an AI model can autonomously decide which tests to run, interpret failures, file issues, and even attempt fixes — all without a human in the loop for routine tasks.

Core Components

1. Test runner with structured output
2. LLM with tool-calling capability
3. Issue tracker integration
4. Code repository access
5. Feedback loop mechanism

The Hard Parts

The biggest challenge isn't the AI — it's the interfaces. Most CI systems and test runners weren't designed to be consumed by agents. You spend 80% of the time building clean tool boundaries.

// Agent tool definition example
{
  name: "run_test_suite",
  description: "Run a named test suite and return structured results",
  parameters: {
    suite: "string",
    tags: "string[]"
  }
}

The agent is only as good as the tools you give it. Garbage interfaces produce garbage decisions.

— Lessons from building VerityGate

Start with one narrow loop — flaky test detection is a great first agent. Once that works reliably, expand scope gradually.