Recruitment teams must review applications, compare qualifications, and decide which candidates should move forward. When applications arrive in large numbers, these steps can take substantial time. Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) can assist with resume screening, skill matching, candidate ranking, and interview preparation. Even so, many current tools address only part of the hiring process. They may provide little explanation for their rankings, overlook skill gaps, offer limited interview personalization, or fail to connect recruitment decisions with employee retention. This paper reviews work on AI-supported recruitment, Robotic Process Automation (RPA), semantic matching, Explainable AI, and predictive HR analytics. It considers where these methods are useful and where their limitations remain. The framework discussed here uses specialized agents for resume analysis, job analysis, skill-gap prediction, interview generation, behavioral response analysis, and retention-risk estimation. Fairness, privacy, clear explanations, and human review are treated as necessary safeguards. Testing with appropriate real-world data will be needed before the framework’s value can be established.