Editor’s note: In a recent topic of Upstream Ag Professional, farming scientist Shane Thomas says for AI agents to be useful in agriculture, they should be part of primary workflow — with ERPs or control point systems, or create a new workflow. Here’s a summary of that content:
There’s transformative possibility of Big Language Model ( LLM) agents in agribusiness, focusing on how they could alter processes and enhance decision-making for specialists in agribusiness. LLM agents are developed AI systems that use huge language models to perform tasks independently, moving beyond standard text generation to tackle interactions, reasoning, and some level of separate action. These agencies can be tailored for specific companies like crops — referred to as “vertical agents” — to better understand and respond to the unique difficulties, speech, and data specific to that field.
The concept of an” OOD A loop” ( Observe, Orient, Decide, Act ), initially developed for military strategy, illustrates how these agents can continuously process information to support goal-oriented tasks in a rapidly evolving environment. Vertical LLM agents in agriculture could simplify tasks like product research, customer relationship management ( CRM ), and marketing. For example, agents may help agronomists recognize and reach out to farmers with certain dirt needs, draft customized marketing messages, or automated CRM data collection. By automating repetitive tasks and reducing manual data entry, these capabilities could save agronomists and sales professionals valuable time.
I also want to draw attention to the significance of” control points” in software, which are central places where important business operations take place. These control points are typically transactional and financial software rather than agronomic software for an agribusiness. LLM agents being incorporated into these control points would transform them from static, record-keeping to dynamic, intelligence-making, enabling real-time decision-making and boosting workflow efficiency.
Looking forward, LLM agents ‘ rise offers a promising solution to labor issues in rural areas, even though a complete transition to AI-driven workflows in agriculture may not occur immediately. As AI agents become more sophisticated and integrated, they are expected to become essential tools for professionals in agriculture, enhancing productivity and decision-making across agribusinesses.
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