Agentic AI for Livestock Housing Management: Applications, Benchmarking, and Readiness Assessment

Agentic artificial intelligence is emerging as an extension of Precision Livestock Farming by linking perception, reasoning, planning, and bounded action within human-supervised livestock-housing workflows. This review synthesizes 90 publications on agentic AI, multi-agent systems, retrieval-augmented generation, large language models, foundation models, robotics, digital twins, simulation, computer vision, cyber-physical control, and related enabling technologies for livestock-housing management. We propose a Perception–Reasoning–Action–Safety (PRAS) loop and an Agentic Livestock Housing Readiness Scale to classify systems from passive monitoring and advisory decision support to supervised, safety-constrained closed-loop operation. A staged benchmarking perspective is also used to integrate algorithmic performance, biological relevance, safety, auditability, economic feasibility, and human–AI interaction. Current evidence is strongest for perception, advisory reasoning, natural-language data access, welfare-risk interpretation, and simulation-supported decision support, whereas robust barn-wide autonomous control remains largely unvalidated. Technology categories were coded non-exclusively; therefore, publication frequencies indicate representation within the selected corpus rather than effectiveness, evidence strength, or deployment readiness. Across species, dairy cattle provide the most developed evidence base, poultry studies mainly address environmental comfort and nutrition support, swine systems emphasize simulation-based precision feeding, and small-ruminant evidence remains concentrated in advisory tools and contextual embodied monitoring. Overall, agentic AI in livestock housing is currently more mature as an orchestration, explanation, and decision-support layer than as an autonomous control technology. By distinguishing direct housing applications, semi-agentic prototypes, and enabling technologies, this review clarifies the gap between current evidence and deployable autonomy. Progress towards higher readiness will require cross-farm validation, biological plausibility, source-grounding audits, safety assurance, interoperability, economic assessment, transparent benchmarking, and explicit human oversight.