Abstract
This disclosure describes an Adaptive Agent Runtime Environment (AARE) architecture. The AARE architecture (AARE) addresses several critical limitations and unmet needs in the burgeoning field of Large Language Model (LLM) integration into complex computational systems. AARE provides a comprehensive synthesis of various current technologies as well new concepts, including integration of various technologies. It seeks to overcome the limitations of existing approaches by offering a tightly integrated framework that natively supports autonomous agents, leverages the strengths of LLMs and symbolic reasoning, provides robust memory management, fosters extensibility through a plugin ecosystem, ensures security and isolation, and facilitates discovery and distribution through a marketplace. AARE addresses the current lack of a holistic and well-architected platform specifically designed to harness the full potential of LLMs and autonomous agents in a user-friendly and scalable manner. In essence, AARE provides a comprehensive and well-architected solution to overcome the fragmentation, limitations, and complexities associated with building advanced applications and deploying autonomous agents powered by Large Language Models. It seeks to establish a foundational platform that enables greater modularity, extensibility, manageability, reasoning capabilities, security, and ecosystem development within this rapidly evolving field.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Ayyagari, Krishna Chytanya, "Hybrid Reasoning System for a Large Language Model Operating System Incorporating Symbolic Inference and Knowledge Graph Integration", Technical Disclosure Commons, (June 03, 2025)
https://www.tdcommons.org/dpubs_series/8189