The emergence of agentic artificial intelligence (AI) systems introduces a significant transformation in the conventional understanding of digital identity and authentication. Unlike conventional software applications, agentic AI systems can perceive environments, reason over contextual information, invoke external tools, communicate with other agents, and execute actions with varying degrees of autonomy. Consequently, authentication must establish not only the identity of an initiating entity but also the identity, authority, behavioral context, and trustworthiness of an autonomous agent acting on behalf of a user or organization. This paper develops an identity-aware authentication taxonomy for agentic AI ecosystems by synthesizing concepts from authentication, reinforcement learning, multi-agent systems, safe AI, causal reasoning, and neuro-symbolic computing. The methodology organizes authentication mechanisms according to identity representation, protocol interaction, contextual verification, threat model, and trust decision. The proposed framework distinguishes static identity authentication, delegated identity authentication, contextual authentication, continuous behavioral authentication, and multi-agent trust authentication. It further categorizes threats into impersonation, credential misuse, agent substitution, delegation abuse, behavioral manipulation, and inter-agent trust exploitation. The analysis demonstrates that authentication for agentic AI cannot be adequately represented as a one-time binary verification event. Instead, authentication should operate as a continuous, risk-aware decision process that integrates identity evidence, contextual signals, authorization constraints, behavioral observations, and trust evolution. The paper contributes a conceptual taxonomy and research framework for designing identity-aware authentication mechanisms capable of supporting autonomous and multi-agent AI environments.

Agentic AI, Identity-Aware Authentication, AI Agents, Authentication Protocols

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