Can QA Reignite its Purpose in the Agentic Code Generation Era?

AI agents now generate an estimated 41% of all code, fundamentally changing QA's role in the SDLC. The bottleneck has shifted from test creation to test execution reliability. Agentic systems amplify flakiness because non-deterministic model outputs cause test logic to vary between runs, and agents treat every failure as a real bug signal—leading them to generate incorrect fixes for infrastructure-induced false failures. The core argument is that traditional enterprise QA infrastructure was designed for human-paced, shared environments, not continuous autonomous execution. For agentic QA to work, three prerequisites must hold: deterministic execution, fully isolated and reproducible environments, and signals that converge toward correctness. The infrastructure gap—not a tooling gap—is identified as the primary constraint blocking reliable agentic QA.