The nature of agency: designing agentic systems using a biomimetic lens

Purpose – This paper aims to explore how biomimetic principles can inform governance models for agentic artificial intelligence (AI) systems, autonomous, adaptive entities that challenge traditional oversight frameworks. It argues that nature-inspired governance offers a dynamic alternative to static, compliance-based models. Design/methodology/approach – This study adopts a conceptual viewpoint approach. It synthesizes literature on AI governance, systems theory and biomimicry, applying thematic analysis to existing frameworks and mapping identified gaps to five natural principles: symmetry, fractals, cymatic feedback, self-organization and phase transitions. Findings – Current governance frameworks lack mechanisms for managing emergent behaviors and distributed agency in agentic AI. The proposed biomimetic lens offers a conceptual scaffold for adaptative, decentralized governance aligned with ethical norms. Research limitations/implications – No empirical validation is provided; future research should use simulation or design science to test biomimetic governance in real-world contexts. Practical implications – This paper offers actionable guidance for policymakers and system designers to adaptive, resilient governance mechanisms into agentic AI architectures. Originality/value – Introduces “Biomimic AI” as a novel paradigm for governing agentic systems, extending systems theory and responsible AI discourse through nature-inspired design logic.