Adaptive Digital Marketing: A Systematic Review of Bio-Inspired Reinforcement Learning, Multi-Agent Systems, and Agentic AI for Intelligent Optimisation

Background: Digital marketing increasingly functions as a complex adaptive system characterised by non-stationary environments, strategic interaction, and multi-agent competition. Programmatic advertising exemplifies this complexity, where decisions must be made in real time under uncertainty. Under such conditions, traditional static optimisation methods often fail to deliver robust performance. This review synthesises bio-inspired computational approaches, reinforcement learning (RL), multi-agent reinforcement learning (MARL), and agentic artificial intelligence (AI) to develop an integrated theoretical perspective on adaptive optimisation in digital marketing. Methods: Following PRISMA 2020 guidelines, we conducted a systematic search of peer-reviewed research across six databases: Scopus, IEEE Xplore, ACM Digital Library, SpringerLink, ScienceDirect, and arXiv, supplemented by manual reference checking. Each computational paradigm is explicitly grounded in foundational biological literature, including work on evolution, foraging, swarm intelligence, and immune cognition. Reinforcement learning supports adaptive decision-making through mechanisms closely aligned with operant conditioning and foraging behaviour. Multi-agent reinforcement learning extends these principles to interactive marketing ecosystems via decentralised coordination and swarm-based learning. Agentic AI further advances adaptive capability by introducing goal-directed reasoning, memory, and higher-level decision orchestration. Contributions: The review identifies persistent fragmentation across marketing sub-domains and a lack of formal mathematical grounding for widely used bio-inspired analogies. To address these gaps, the study proposes a multi-layer bio-inspired framework and outlines a structured research agenda to guide the development of autonomous digital marketing systems.