Empowering Agent-based AI: An Innovative Framework for Model Execution and Interoperability

ntroduction: The growing dependence on predictive modeling across various sectors including research, product development, and risk assessment highlights the need for

an infrastructure that supports consistent execution across diverse operating systems
and computational settings. We introduce an advanced framework that integrates the
FAIR Scientific Knowledge eXchange (FSKX) standard with prevalent open-source
communication protocols and an innovative encapsulation strategy.
Goals: Our project is focused on developing a flexible, cloud-based infrastructure for
running FSKX models, resolving dependency issues by creating custom encapsulated
environments unique to each model's needs. Through the adoption of the EPCIS 2.0
(Electronic Product Code Information Services) standard, our system promotes effective
interaction between discrete application programming interfaces (APIs) within this
ecosystem.
Encapsulation of FSKX Models: Traditional methods of managing compute environments
often struggled with meeting their specific requirements, which limited their crossplatform
functionality. Our strategy creates a series of encapsulated environments,
each corresponding to a single model, hence providing the specific computational
context required.
Communication via EPCIS API: At the heart of our architecture is a flexible framework
that connects to any FSKX model repository, enabling access to and deployment of
models via standard APIs. This design allows service providers and research groups to
develop tailored user interfaces while maintaining a consistent core infrastructure.
Conclusion: Our method is set to enhance the potential of community-supported model
repositories by providing well-defined, conflict-free computational environments for a
wide range of model applications. In addition, the API framework we've developed can
also serve as a valuable tool for agent-based AI and large language model (LLM)
technology, offering a robust, secure, and adaptable foundation for model execution
across supply chain networks.
This research has been funded by the German Federal Ministry of Food and Agriculture
(BMEL) in the research project “KI- & Daten-Akzelerator (KIDA)” with project number
28KIDA004.