TreeAgent: Multi-agent framework for automatic labeling of forest biases

In sectors such as forest remote sensing, classifying biases in tree height requires expert labeling that, besides being costly, suffers from high variability among annotators. This bottleneck hinders the scalability of machine learning models, especially when precision and consistency are required. An innovative solution combines the robustness of decision trees based on expert knowledge with the flexibility of vision-language models (VLMs), creating a multi-agent system capable of replicating labeling procedures at a significantly lower cost. This approach, known as TreeAgent, demonstrates that artificial intelligence for businesses can integrate prior knowledge in a structured way, maintaining interpretability and reducing dependence on human annotations. Instead of training supervised models with large volumes of labeled data, agents are orchestrated to vote among themselves to mitigate the stochasticity of VLMs, achieving superior results without modifying the knowledge base. This architecture is not only applicable to classifying forest biases but also lays the groundwork for automating complex processes in domains where expert judgment is critical, such as medicine or industry. At Q2BSTUDIO, we develop custom applications that incorporate AI agents for labeling, analysis, and decision-making tasks, combining them with cloud platforms such as AWS and Azure cloud services to scale without friction. Additionally, we integrate business intelligence and Power BI capabilities to visualize the results of these multi-agent systems, allowing organizations to audit and refine their labeling processes. Cybersecurity also plays a key role in protecting sensitive data flowing between agents and expert repositories. In short, TreeAgent represents a paradigm where custom software and AI for businesses align to overcome the limitations of manual labeling, offering a path toward responsible and efficient automation.