arXiv AI By Constantinos Papantoniou, Brian Hilton

ANASSA: From High-Level Framework to Low-Level Architecture for Spatial Agentic AI

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arXiv AI
Sep 15

ANASSA: An Agentic AI Orchestration Framework for Spatial Intelligence

The paper introduces ANASSA, an agentic AI orchestration framework designed for spatial intelligence in geographic information systems. It addresses gaps in current systems by integrating structured spatial reasoning, multi‑agent workflow orchestration, execution feedback, authoritative validation, provenance, uncertainty handling, and human decision authority. The architecture is detailed with eleven components across four layers, a six‑step Geospatial AI Cognitive Loop, cross‑component contracts, and governance mechanisms to ensure traceability, reproducibility, and accountability.

By Constantinos Papantoniou, Brian Hilton
arXiv AI
Sep 11

A Taxonomy of Architecture Options for Foundation Model-based Agents: Analysis and Decision Model

The paper presents a taxonomy of architecture options for foundation-model-based agents, covering functional capabilities, non‑functional qualities, and operational aspects of design‑time and run‑time phases. It also introduces a decision model to guide critical design and runtime choices, aiming to streamline and improve the development of such agents. By unifying these classifications, the authors seek to reduce fragmentation in the field and provide a structured framework for architects and developers.

By Jingwen Zhou, Qinghua Lu, Jieshan Chen, Liming Zhu, Xiwei Xu, Zhenchang Xing, Stefan Harrer