The unprecedented increase of complex cyber threats has significantly exposed the limits of classical approaches to penetration testing-often manual, time-consuming activities relying on human expertise. It is challenging for conventional approaches to scale appropriately, dynamically adapt to shifting attack vectors, and comprehensively cover increasingly complex systems. In turn, the ability of organizations to apply countermeasures that target the ever-increasing scale and sophistication of current cyber threats is being significantly stretched. With the development of Artifcial Intelligence and large language models, this opens up very promising possibilities towards solving some of these issues; existing frameworks have nonetheless remained subject to several severe problems, such as insufficient context conservation, inefficient use in multi-step reasoning, inappropriateness for scaling dynamics, and highly important ethical implications because of its deployment and further misuse. This thesis proposes the AutoSecAgent, an advanced framework in automated penetration testing. By embedding advanced capabilities such as recursive memory embedding for long-term context maintenance across multi-step tasks, Retrieval-Augmented Generation in real time, and a modular architecture that can ensure adaptability and scalability, the current system will be overcome. The framework also interacts flawlessly with industrial standard tools like Metasploit and PowerShell, dynamically interacting with other systems to perform complex tasks in penetration testing. AutoSecAgent further improves through reinforcement learning and adaptive feedback mechanisms, refining its decision-making processes toward more optimal execution of tasks over time while minimizing computational overhead. Key issues include reasoning over extended attack chains, adapting to diversified and evolving environments, and further improving scalability are addressed by AutoSecAgent which marks a major leap toward fully automated penetration testing with an innovative design that ensures increased task completion rates, improved efficiency, and real-time performance while building a framework for the ethical and responsible application of AI-based solutions in penetration testing. Comprehensive evaluations demonstrate the efficacy of AutoSecAgent in performance compared to existing models-include PentestGPT and AutoAttacker in various axes of efficiency, adaptability, and task successes, while setting new benchmarks for scalability and long-term contextual reasoning. The research underlines how such an AI-powered framework can be fundamentally disruptive to modern cybersecurity as a reliable and future-proof way to help organizations build an effective defense against increasing attacks and protect digital infrastructure.