arXiv AI
Agentic AI Orchestration Framework for Spatial Intelligence: Supply Chain Deployment
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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.
arXiv AI
Oct 15
ANASSA: From High-Level Framework to Low-Level Architecture for Spatial Agentic AI
arXiv:2610.14382v1 Announce Type: new Abstract: Agentic AI systems operating over geospatial data require more than autonomous reasoning and tool use; they must also support explicit orchestration, s...
arXiv AI
Jun 17
Large Language Models for Agentic NetOps and AIOps: Architectures, Evaluation, and Safety
arXiv:2605. 12729v2 Announce Type: replace-cross Abstract: Large language models are increasingly being used to support network operations (NetOps) and artificial intelligence for IT operations (AIOps), including incident investigation, root-cause analysis, configuration synthesis, and limited self-healing.
arXiv AI
Jul 28
Building AI That Works: ESnet's Pragmatic Approach to AI-Driven Operational Excellence
arXiv:2607. 22948v1 Announce Type: cross Abstract: The ORBIT (Operations Responses and Business Intelligence Toolkit) project was initiated to assess agentic AI for the upcoming ESnet 7 initiative and to address persistent operational pain points in the Network Operations Center (NOC) workflow.
arXiv AI
Jul 9
Progressive Crystallization: Turning Agent Exploration into Deterministic, Lower-Cost Workflows in Production
arXiv:2607. 07052v1 Announce Type: cross Abstract: AI agents deployed for IT operations are typically permanent cost centers because every execution requires full LLM inference, even for previously solved problems.
arXiv AI
Jul 21
Agentic ERP: Multi-Agent Large Language Model Architecture for Autonomous Enterprise Resource Planning
arXiv:2607. 17331v1 Announce Type: new Abstract: Enterprise Resource Planning (ERP) systems record transactions reliably but still delegate almost all operational decision-making to human specialists, because classical rule-based automation cannot reason about exceptions and monolithic AI assistants degrade when asked to coordinate across functional boundaries.