Autonomous AI Agents for Adaptive Test Intelligence in Large-Scale Healthcare Systems

Abstract

The increasing complexity of cloud-native healthcare systems necessitates intelligent, scalable, and compliance-aware testing strategies. Traditional automation frameworks struggle to adapt to dynamic microservices architectures, real-time data processing, and stringent regulatory requirements. This paper presents a novel approach leveraging autonomous AI agents to enable adaptive test intelligence in large-scale healthcare environments. The proposed framework integrates agent-based orchestration with machine learning and large language models (LLMs) to dynamically generate, execute, and optimize test scenarios across distributed systems. Implemented in a real-world healthcare platform supporting over 200,000 patient workflows annually, the framework demonstrates measurable improvements, including a 60% reduction in manual testing effort, a 30% acceleration in release cycles, and enhanced compliance with zero protected health information (PHI) exposure incidents. This research establishes a foundation for autonomous quality engineering systems and highlights the transformative potential of AI-driven testing in regulated industries.

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