# On the potential of agentic workflows for animal training plan generation

## TL;DR

This paper proposes using agentic workflows with AI to create customizable and actionable animal training plans, addressing the limitations of current generative AI tools.

## Contribution

A modular agentic workflow framework is introduced to generate tailored animal training plans using large language models.

## Key findings

- A proof-of-concept workflow demonstrates the feasibility of using autonomous agents for structured training plan generation.
- The modular design allows customization to specific training tasks and philosophies.
- The approach ensures compliance with welfare standards and team-specific procedures.

## Abstract

Effective animal training depends on well-structured training plans that ensure consistent progress and measurable outcomes. However, the creation of such plans is often time-intensive, repetitive, and detracts from hands-on training. Recent advancements in generative AI powered by large language models (LLMs) provide potential solutions but frequently fail to produce actionable, individualized plans tailored to specific contexts. This limitation is particularly significant given the diverse tasks performed by dogs–ranging from working roles in military and police operations to competitive sports–and the varying training philosophies among practitioners. To address these challenges, a modular agentic workflow framework is proposed, leveraging LLMs while mitigating their shortcomings. By decomposing the training plan generation process into specialized building blocks–autonomous agents that handle subtasks such as structuring progressions, ensuring welfare compliance, and adhering to team-specific standard operating procedures (SOPs)—this approach facilitates the creation of specific, actionable plans. The modular design further allows workflows to be tailored to the unique requirements of individual tasks and philosophies. As a proof of concept, a complete training plan generation workflow is presented, integrating these agents into a cohesive system. This framework prioritizes flexibility and adaptability, empowering trainers to create customized solutions while leveraging generative AI's capabilities. In summary, agentic workflows bridge the gap between cutting-edge technology and the practical, diverse needs of the animal training community. As such, they could form a crucial foundation for advancing computer-assisted animal training methodologies.