Agentic Artificial Intelligence in Food Science: A Conceptual Framework and Structured Synthesis for Food Safety, Shelf-Life Extension, and Environmental Sustainability

The global food system faces three converging pressures. Foodborne diseases affect an estimated 600 million people each year. Post-harvest losses and waste exceed 30% of total production. Food systems also generate roughly one-quarter to one-third of global anthropogenic greenhouse gas emissions. Existing artificial intelligence (AI) tools address these pressures only in isolation. Most are narrow, task-specific predictive models. They classify, forecast, or flag. They do not perceive, reason, and act within a single closed loop. This paper proposes a conceptual framework that closes that loop. We define agentic AI as autonomous software entities that perceive their environment, reason over causal and physical models, and execute bounded actions under explicit governance. The framework has three layers: Perception, Reasoning, and Action. A Digital Twin sits at the reasoning core. It simulates candidate interventions before any physical action is taken. The framework is supported by a structured literature synthesis rather than a systematic meta-analysis and we also document the searched databases, search strings, screening steps, and inclusion criteria. All quantitative values reported in this paper are presented as indicative ranges with explicit provenance tags. Each value is labeled by evidence tier: field deployment, laboratory testing, simulation, or secondary literature. No new field trials were conducted for this study. The performance figures should therefore be read as reported potential, not as validated outcomes. The contributions of this work are fourfold. First, we specify a unified Perception–Reasoning–Action architecture that integrates Digital Twins for closed-loop food system optimization. Second, we provide a critical synthesis of prior literature, a source-by-source evidence traceability matrix, and a benchmarking scheme that compares agentic AI against task-specific baselines rather than a single homogeneous baseline. Third, we propose a tiered governance model mapped explicitly to HACCP, ISO 22000, FDA guidance, and the EU AI Act, including an accountability allocation for autonomous food-safety decisions. Fourth, we operationalize Socio-Technical Systems theory into an adoption framework and a staged validation agenda for future empirical work.