Agentic Artificial Intelligence in Forestry: A Survey of Its Development, Applications, Current Opinions and Future Directions

Software has supported forest management decisions since the 1970s, from growth simulators to reinforcement learning. Since 2023, large language models (LLMs) have been combined with tools, retrieval, memory and planning to form artificial intelligence (AI) agents that analyse forest data, decide on a sequence of steps, and either advise a person or act through software and machines. This survey reviews agentic AI in forestry. It traces the development of agent concepts in the field, describes the components that current agentic systems share, and reviews applications in seven areas, from forest inventory to biodiversity monitoring. We found only 13 LLM-based agentic systems in forestry. Most are single LLM agents or small groups of role-based agents that coordinate perception models, geospatial databases and retrieval over documents. Their evaluation relies on case studies, small expert panels and public datasets, and none of the reviewed LLM-based agents controls forest machines in the field. Recurring problems are factual errors, weak spatial reasoning, missing connectivity in forests, the cybersecurity of connected machines and trust, and current evaluations are short compared with the decades over which forest decisions take effect. We derive research directions on evaluation with forest simulators, edge deployment, interoperability, graded human oversight and safety constraints for forest machines. A comparison with agriculture identifies designs that can be transferred, and we expect agents to become routine first in analysis and advice, then in planning with forest models, and only later in the supervision of forest machines.