The End-to-End Autonomous QA Agent: Orchestrating Discovery, Execution, and Defect Triage

01. The Problem

02. The Concrete Engineering Scenario

A new staging build of WrightPay is deployed. The autonomous QA system must explore the application, verify core transactional invariants, detect regressions, gather forensic evidence, and present a human-readable quality verdict.

03. Context & Inputs Provided to Agent

• •Staging deployment URL and test environment credentials.
• •OpenAPI spec, database connection string, and BullMQ Redis URL.
• •Baseline test inventory (608 verified tests) and known defect register.

04. Agent Reasoning & Planning Steps

// Conceptual Multi-Agent QA Orchestrator
interface AutonomousQARunner {
  readonly planner: TestPlannerAgent;
  readonly driver: PlaywrightExecutionAgent;
  readonly forensics: ForensicAnalysisAgent;
  readonly reporter: DocumentationAgent;

  async executeReleaseAudit(targetEnv: EnvironmentConfig): Promise<QualityVerdict> {
    // 1. Plan coverage based on environment diff
    const testPlan = await this.planner.plan(targetEnv.spec);

    // 2. Drive Playwright execution with live DOM feedback
    const testResults = await this.driver.execute(testPlan);

    // 3. Correlate traces, logs, and database state for failures
    const diagnosedDefects = await this.forensics.triage(testResults.failures);

    // 4. Synthesize human-ready quality sign-off document
    return this.reporter.generateSignOff(testResults, diagnosedDefects);
  }
}