The increasing autonomy of agentic artificial intelligence (AI) systems introduces a fundamental challenge for identity security because conventional identity and access mechanisms are primarily designed around relatively stable human users, applications, and service accounts. Agentic AI systems can dynamically select tools, invoke services, exchange information, and perform multi-step actions with limited human intervention. Consequently, identity security must evolve from static authentication toward adaptive mechanisms capable of continuously evaluating identity, context, authorization, behavioral patterns, and operational risk. This paper proposes an Adaptive Identity Security Framework for Agentic AI Systems that integrates identity representation, contextual authentication, dynamic authorization, behavioral monitoring, risk-based policy evaluation, and continuous identity assurance. The framework is conceptually positioned around an identity-fabric approach in which agent identities remain traceable across heterogeneous services and dynamically changing execution contexts. The methodology defines architectural layers, an adaptive authorization model, an identity-risk evaluation process, and an implementation workflow. The supplied literature is also critically examined to identify transferable methodological insights concerning artificial intelligence, automated decision systems, model reliability, and evaluation. The analysis indicates that adaptive identity security can reduce excessive privilege, improve action traceability, and provide stronger controls over autonomous AI operations, although it introduces computational overhead, policy complexity, identity-provenance challenges, and potential false-positive authorization decisions. The framework extends the identity-fabric perspective proposed by Pappu, Bhushan, and Jaiswal by emphasizing continuous adaptation and performance evaluation as core properties of agentic AI identity management.