The core concepts of market transactions are being redefined as AI moves beyond reaction to self-autonomous software proxies. This paper proposes a conceptual framework of "Algorithmic Consumer," where the human buyer completely entrusts the purchasing decision, evaluation, and execution to Agentic AI. Although conventional marketing literature has described human actors interacting within choice architectures, using cognitive heuristics, emotional affect, and brand stories, agentic commerce invokes the dynamics of the machine interacting within choice architectures, maximizing utility. We synthesize the concepts of service-dominant logic, bounded rationality, agency theory, and cognitive offloading and apply them in the context of consumer behaviour, strategic management, and software engineering. Technical tricks, such as machine autonomy, recursive learning protocols, and genetic algorithms, are embedded and are used to show how the consumer proxies mathematically optimize preference profiles. Hence, a paradigm shift is needed in corporate strategy from CRM to Machine-to-Machine Relationship Management (M2MRM), and e-commerce becomes a field of programmatic and technical interoperability. Lastly, we critically review the "dark side" of delegation, discussing the double principal-agent problem, loss of consumer autonomy, algorithmic bias, and platform-level utility exploitation, and propose a strategic roadmap and an agenda for future empirical research. Keywords: Algorithmic Consumer, Agentic AI, Machine-to-Machine Relationship Management (M2MRM), Genetic Algorithms, Bounded Rationality, Service-Dominant Logic, Choice Delegation.