QA Arbiter Tool Forces AI Agents to Prove Reasoning Before Flagging Test Failures

A developer has built a tool called QA Arbiter to address a recurring problem in AI-driven CI pipelines, where language models misdiagnose test failures and trigger endless fix-retry loops. The core issue, termed 'Assertion Confusion,' occurs when an AI agent incorrectly attributes a broken test assertion to faulty business logic, causing it to refactor working code. QA Arbiter tackles this by enforcing a structured 'Decision Pivot' pattern, requiring agents to trace every calculation step using actual test inputs before committing to a diagnosis. The tool then compares received values and expected assertions against that trace, producing a deterministic verdict — either a test error or an engine defect — with no ambiguous outcomes. If an agent's conclusion contradicts its own trace, the tool rejects the call entirely, forcing the agent to re-examine its reasoning before any code changes are made.