A Hybrid Agentic AI Framework for Intelligent Supply Chain Analytics

• URL: http://arxiv.org/abs/2609.13561v1
• Date: Fri, 11 Sep 2026 21:59:30 GMT
• Title: A Hybrid Agentic AI Framework for Intelligent Supply Chain Analytics
• Abstract summary: We propose an agentic system for supply chain analytics that bridges the gap between business decision-making and technical expertise.<n>The system supports both exploratory analysis and deterministic delegates, enabling planners to transition between ad hoc questions and structured processes.<n>Results show that our multi-agent design achieves a 90% accuracy, which is competitive with a single agent baseline.
• Score: 3.3292687565862535
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Efficient utilization of supply chain analytics for decision making remains a significant challenge for planners, as critical tasks such as database querying, key performance indicator (KPI) analysis, demand forecasting, and performance diagnosis require heterogeneous expertise spanning data engineering, operations research, and domain knowledge. In this work, we propose an agentic system for supply chain analytics that bridges the gap between business decision-making and technical expertise, where a coordinator agent interprets user intent and delegates sub-tasks to specialized agents. The system supports both exploratory analysis and deterministic workflows, enabling planners to transition between ad hoc questions and structured processes. Domain logic is encapsulated within specialist agents and prompts, yielding a scalable, modular, and auditable design and lowering the cost of functional extension through prompt-centric development. We evaluate the proposed architecture on a test environment that replicates multi-echelon inventory management operations. Results show that our multi-agent design achieves a 90\% accuracy, which is competitive with a single agent baseline while reducing input token usage by roughly fourfold, substantially improving scalability and cost-efficiency. Furthermore, we provide case studies to demonstrate interpretable suboptimality detection and automated forecast optimization, illustrating how agentic architectures can effectively combine open-ended exploratory analysis and deterministic supply chain analytics workflows, and provide a practical pathway toward more accessible and extensible decision-support systems.

Related papers

• The Optimizer Is the Agent: Reasoning-Driven Search across Prompts, Programs, and ML Workflows [72.9051182568928]
  We present ReASearch, a unified framework for reasoning-driven optimization.<n>With a shared agent loop and domain-specific tools, ReASearch instantiates the exact same scaffold to optimize prompts, programs, and ML.<n>We observe that complex search behaviors emerge naturally from the agent's reasoning process.
  arXiv  Detail & Related papers  (2026-08-07T02:15:39Z)
• Adaptation of Agentic AI [162.63072848575695]
  We unify the rapidly expanding research landscape into a systematic framework that spans both agent adaptations and tool adaptations.<n>We demonstrate that this framework helps clarify the design space of adaptation strategies in agentic AI.<n>We then review the representative approaches in each category, analyze their strengths and limitations, and highlight key open challenges and future opportunities.
  arXiv  Detail & Related papers  (2025-12-18T08:38:51Z)
• A Comprehensive Survey on Benchmarks and Solutions in Software Engineering of LLM-Empowered Agentic System [56.40989626804489]
  This survey provides the first holistic analysis of Large Language Models-powered software engineering.<n>We review over 150 recent papers and propose a taxonomy along two key dimensions: (1) Solutions, categorized into prompt-based, fine-tuning-based, and agent-based paradigms, and (2) Benchmarks, including tasks such as code generation, translation, and repair.
  arXiv  Detail & Related papers  (2025-10-10T06:56:50Z)
• PowerChain: A Verifiable Agentic AI System for Automating Distribution Grid Analyses [1.9682121767943495]
  Rapid electrification and decarbonization are increasing the complexity of distribution grid (DG) operation and planning.<n>These analyses depend on disparate models, function calls, and data pipelines that require expert knowledge and remain difficult to automate.<n>We build an agentic system PowerChain, which is capable of autonomously performing complex grid analyses.
  arXiv  Detail & Related papers  (2025-08-23T17:24:46Z)
• I2I-STRADA -- Information to Insights via Structured Reasoning Agent for Data Analysis [0.0]
  Real-world data analysis requires a consistent cognitive workflow.<n>We introduce I2I-STRADA, an agentic architecture designed to formalize this reasoning process.
  arXiv  Detail & Related papers  (2025-07-23T18:58:42Z)
• Deep Research Agents: A Systematic Examination And Roadmap [109.53237992384872]
  Deep Research (DR) agents are designed to tackle complex, multi-turn informational research tasks.<n>In this paper, we conduct a detailed analysis of the foundational technologies and architectural components that constitute DR agents.
  arXiv  Detail & Related papers  (2025-06-22T16:52:48Z)
• Agent-based Condition Monitoring Assistance with Multimodal Industrial Database Retrieval Augmented Generation [3.8451399765175016]
  Condition monitoring (CM) plays a crucial role in ensuring reliability and efficiency in the process industry.<n>This work integrates large language model (LLM)-based reasoning agents with CM to address analyst and industry needs.<n>We propose MindRAG, a modular framework combining multimodal retrieval-augmented generation (RAG) with novel vector store structures designed specifically for CM data.
  arXiv  Detail & Related papers  (2025-06-10T21:04:18Z)
• The Cost of Dynamic Reasoning: Demystifying AI Agents and Test-Time Scaling from an AI Infrastructure Perspective [3.0868637098088403]
  Large-language-model (LLM)-based AI agents have recently showcased impressive versatility by employing dynamic reasoning.<n>This paper presents the first comprehensive system-level analysis of AI agents, quantifying their resource usage, latency behavior, energy consumption, and test-time scaling strategies.<n>Our findings reveal that while agents improve accuracy with increased compute, they suffer from rapidly diminishing returns, widening latency variance, and unsustainable infrastructure costs.
  arXiv  Detail & Related papers  (2025-06-04T14:37:54Z)
• Agentic Predictor: Performance Prediction for Agentic Workflows via Multi-View Encoding [56.565200973244146]
  Agentic Predictor is a lightweight predictor for efficient agentic workflow evaluation.<n>By learning to approximate task success rates, Agentic Predictor enables fast and accurate selection of optimal agentic workflow configurations.
  arXiv  Detail & Related papers  (2025-05-26T09:46:50Z)
• Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey [58.50944604905037]
  Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications.<n>Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems.<n>This survey provides a structured tutorial on fundamental architectures, enabling technologies, and emerging applications.
  arXiv  Detail & Related papers  (2025-05-03T13:55:38Z)
• Data Analysis in the Era of Generative AI [56.44807642944589]
  This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
  arXiv  Detail & Related papers  (2024-09-27T06:31:03Z)
• Towards Human-Level Understanding of Complex Process Engineering Schematics: A Pedagogical, Introspective Multi-Agent Framework for Open-Domain Question Answering [0.0]
  In the chemical and process industries, Process Flow Diagrams (PFDs) and Piping and Instrumentation Diagrams (P&IDs) are critical for design, construction, and maintenance. Recent advancements in Generative AI have shown promise in understanding and interpreting process diagrams for Visual Question Answering (VQA) We propose a secure, on-premises enterprise solution using a hierarchical, multi-agent Retrieval Augmented Generation (RAG) framework.
  arXiv  Detail & Related papers  (2024-08-24T19:34:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.