The increasing complexity of cloud-native, distributed, and AI-enabled software systems has rendered traditional, static quality assurance (QA) practices increasingly inadequate, as fixed test suites and manually curated strategies struggle to keep pace with rapid release cycles, architectural heterogeneity, and continuously evolving system behavior. Although automated test generation techniques such as random testing, symbolic execution, and evolutionary algorithms have matured considerably since the early 2000s, they are typically deployed as standalone tools with limited awareness of broader quality objectives, execution cost, or contextual risk, thereby constraining their effectiveness in large-scale, real-world environments. Recent advances in large language models (LLMs), reinforcement learning, and agent-based system design enable a fundamentally new paradigm in which testing capabilities are embedded within autonomous quality agents that can reason, adapt, and learn over time. This article introduces a policy-driven autonomous quality agent framework that unifies classical test generation techniques with modern AI-based orchestration, drawing on established systems such as EvoSuite and KLEE alongside recent LLM-based architectures like TESTPILOT. By incorporating feedback-aware control loops, cost- and risk-sensitive policies, and iterative refinement mechanisms, the proposed approach enables test generation strategies to be dynamically selected, combined, and optimized in response to system changes and empirical outcomes. As a result, autonomous quality agents emerge as first-class actors within modern CI/CD pipelines, continuously optimizing coverage, defect detection, and resource utilization under realistic operational constraints rather than operating as passive, one-off automation tools.