The concept of the 'agent' has profoundly shaped Artificial Intelligence (AI) research, guiding development from foundational theories to contemporary applications like Large Language Model (LLM)-based systems. This paper critically re-evaluates the necessity and optimality of this agent-centric paradigm. We argue that its persistent conceptual ambiguities and inherent anthropocentric biases may represent a limiting framework. We distinguish between agentic systems (AI inspired by agency, often semi-autonomous, e.g., LLM-based agents), agential systems (fully autonomous, self-producing systems, currently only biological), and non-agentic systems (tools without the impression of agency). Our analysis, based on a systematic review of relevant literature, deconstructs the agent paradigm across various AI frameworks, highlighting challenges in defining and measuring properties like autonomy and goal-directedness. We argue that the 'agentic' framing of many AI systems, while heuristically useful, can be misleading and may obscure the underlying computational mechanisms, particularly in Large Language Models (LLMs). As an alternative, we propose a shift in focus towards frameworks grounded in system-level dynamics, world modeling, and material intelligence. We conclude that investigating non-agentic and systemic frameworks, inspired by complex systems, biology, and unconventional computing, is essential for advancing towards robust, scalable, and potentially non-anthropomorphic forms of general intelligence. This requires not only new architectures but also a fundamental reconsideration of our understanding of intelligence itself, moving beyond the agent metaphor.