Generative Artificial Intelligence (Gen-AI) can enhance human-computer interaction by enabling natural, adaptive, and personalized communication to significantly improve data integrity and quality through automation and flexibility. Meta Ag, an Android-based farm recordkeeping app was developed to address challenges of error-prone manual data entry and it automatically recorded spatial and temporal information of farm operations; it employed a rule-based chatbot with data validation mechanisms to document operational details. In this work, the capabilities of Meta Ag were extended by incorporating a retrieval augmented agentic-AI framework to facilitate precise and complete metadata recording. The designed framework utilizes Gen-AI capabilities through API to collect and validate metadata from a user maintaining cognitively aligned human-AI interaction. The framework uses the data storage backend as a retrievable knowledge base and creates contextually appropriate questions for the user about detailed farm activity, replacing the rule-based chatbot in Meta Ag app. Implementing this framework, an example web application, Meta Ag 2.0, was developed where ChatGPT (GPT-4o) serves as the agentic AI engine. The app automatically transmits recorded spatial and temporal data to the AI, which applies procedural memory to query a private Airtable database (knowledge base, backend) to access pre-defined metadata fields. The AI dynamically generates context-specific questions, validates user inputs against database options, and refines metadata accuracy through iterative interaction before writing data records. This AI-enhanced recordkeeping system minimizes errors, reduces complexity, and enables the seamless documentation of diverse data through robust, scalable and human-centric data management solutions. This approach enables richer contextual data to support digital agriculture advancements.
Schematic of Meta Ag 2.0 framework, illustrating the sequence of steps represented by numbered stages.