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ANASSA: An Agentic AI Orchestration Framework for Spatial Intelligence — arXiv:2609.14824

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.14824
License (as archived): CC0-1.0
Archived: 2026-10-15T22:47:38.455Z
SHA-256 of the archived text: 362b4904327e0beef89f27fbf2b4ab084a4a41c4d228d54890aa8a7110cd21e5
Stable URL of this page: https://metraipolis.com/documento/dc_68bfdffd343e

How to cite

ANASSA: An Agentic AI Orchestration Framework for Spatial Intelligence — arXiv:2609.14824 — arXiv (en), https://arxiv.org/abs/2609.14824 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_68bfdffd343e (Metraipolis archive dc_68bfdffd343e, sha256 362b4904327e0beef89f27fbf2b4ab084a4a41c4d228d54890aa8a7110cd21e5)

BibTeX

@misc{dc68bfdffd343e,
  title = {ANASSA: An Agentic AI Orchestration Framework for Spatial Intelligence — arXiv:2609.14824},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.14824},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_68bfdffd343e; sha256 362b4904327e0beef89f27fbf2b4ab084a4a41c4d228d54890aa8a7110cd21e5},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.14824. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

The emergence of large language models (LLMs) and large multimodal models (LMMs) has enabled a new class of agentic systems capable of integrating natural language understanding with tool-based execution. In geographic information systems (GIS), this shift is transforming traditional, expert-driven workflows into semiautonomous systems that can interpret user intent, construct spatial workflows, and execute geospatial analysis tasks. However, existing approaches remain limited by fragmented integration of reasoning, execution, and evaluation, particularly in complex, real-world environments. This study synthesizes recent advances in agentic GIS frameworks, benchmarks, and surveys to identify limitations in spatial reasoning, execution robustness, validation, governance, and evaluation. Building on these insights, it introduces ANASSA (Autonomous Neural Agents for Spatial Systems Architecture), an agentic AI orchestration framework that integrates structured spatial reasoning, multi-agent workflow orchestration, execution feedback, authoritative spatial validation, provenance, uncertainty handling, and human decision authority within a unified system design. The contribution is an architecture-level specification: eleven components across four layers, a six-step Geospatial AI Cognitive Loop, cross-component contracts, and governance mechanisms intended to make agentic geospatial workflows traceable, reproducible, and accountable. Empirical performance evaluation is reserved for implementation and deployment studies.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-15 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.14824 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-15T22:29:03.220Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-15?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).