Agentic ERP: Multi-Agent Large Language Model Architecture for Autonomous Enterprise Resource Planning

• URL: http://arxiv.org/abs/2607.17331v1
• Date: Sun, 19 Jul 2026 16:40:41 GMT
• Title: Agentic ERP: Multi-Agent Large Language Model Architecture for Autonomous Enterprise Resource Planning
• Abstract summary: Agentic ERP is an expert-system architecture that combines role-aligned large-language-model (LLM) agents with a risk-tiered human-in-the-loop harness.<n>The system is evaluated at three levels: a scenario-based task suite, a comparison of six orchestration paradigms on cross-functional crisis tasks, and a 365-day agent-in-the-loop simulation.
• Score: 15.444798962796158
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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. This paper presents Agentic ERP, an expert-system architecture that combines role-aligned large-language-model (LLM) agents with a risk-tiered human-in-the-loop harness and a graph-based orchestrator to execute end-to-end business workflows on a production ERP backend. First, autonomous ERP operation is formulated as a constrained sequential-decision problem over a structured enterprise state, with a decomposition argument linking role-aligned agents to a measurable reduction in per-step tool-selection complexity. Second, a graph-based Planner--Executor--Reflector--Responder orchestration decouples generation from evaluation through externalised grading criteria and sprint contracts, packaging recent harness-engineering principles as inspectable expert-system artefacts. Third, the system is evaluated at three levels: a scenario-based task suite, a comprehensive comparison of six orchestration paradigms on cross-functional crisis tasks, and a 365-day agent-in-the-loop simulation against rule-based RPA and no-intervention baselines. Across these levels the proposed multi-agent method is significantly better than the baseline, and the system sustains a simulated year of operation with zero stockouts while the rule-based baseline accumulates hundreds under the same demand stream. The work shows that role-aligned LLM agents under human oversight can move an ERP system from passively recording transactions to actively executing operational decisions, and it provides a reference architecture and an evaluation protocol for autonomous enterprise resource planning.

Related papers

• EASy: Towards Efficient LLM-Based Agentic System [54.10819421625103]
  We propose EASy, a trainable agentic framework that jointly optimize task performance and computational efficiency through reinforcement learning.<n> EASy equips an LLM-based orchestrator with explicit knowledge of the capability and cost profiles of heterogeneous executors, enabling context-sensitive coordination beyond performance-only routing.<n>It further introduces a milestone-plan-act workflow that decomposes complex tasks into manageable milestones, constructs dependency-aware execution graphs, assigns suitable executors, and parallelizes independent steps while adapting subsequent decisions to intermediate outcomes.
  arXiv  Detail & Related papers  (2026-08-05T08:50:36Z)
• Agentic AI and Retrieval-Augmented Models in Straight-Through Underwriting [7.670757733138505]
  Actuaries now face a design space that ranges from traditional rule-based automation to large language models.<n>This paper examines how these emerging architectures can support actuarial priorities such as transparency, auditability, and human-in-the-loop governance.
  arXiv  Detail & Related papers  (2026-07-08T18:43:34Z)
• Orchestra-o1: Omnimodal Agent Orchestration [72.34907006336108]
  Orchestra-o1 is an omnimodal agent orchestration framework designed to support efficient agent collaboration across multiple modalities.<n>This work introduces a unified orchestration mechanism that enables modality-aware task decomposition, online sub-agent specialization, and parallel sub-task execution.<n>This scalable design allows agent systems to effectively tackle complex real-world tasks involving heterogeneous information sources, surpassing the second-best approach by 10.3% accuracy on the OmniGAIA benchmark.
  arXiv  Detail & Related papers  (2026-06-10T04:50:35Z)
• Beyond Autonomy: A Dynamic Tiered AgentRunner Framework for Governable and Resilient Enterprise AI Execution [1.509090088899154]
  We propose the Dynamic Tiered AgentRunner, a controlled execution protocol distilled from a production-grade multi-tenant platform.<n>The framework introduces three core mechanisms: (1) Risk-Adaptive Tiering that dynamically allocates computational resources and review intensity based on task risk profiles; (2) Separation of Powers architecture where proposal, review, execution, and verification are performed by independent agents with physically isolated boundaries; and (3) Resilience-by-Design through a Verifier-Recovery closed loop that treats failure as a first-class system state.
  arXiv  Detail & Related papers  (2026-05-11T09:03:48Z)
• Decision-Theoretic Safety Assessment of Persona-Driven Multi-Agent Systems in O-RAN [0.2873445316250935]
  We introduce a persona driven multi agent framework for autonomous network management.<n>We evaluate 486 persona configurations across two ORAN optimization challenges.<n>Results demonstrate that persona agent alignment significantly impacts both individual performance and emergent multi agent coordination.
  arXiv  Detail & Related papers  (2026-04-03T15:20:16Z)
• OrgAgent: Organize Your Multi-Agent System like a Company [75.47076168155817]
  We introduce OrgAgent, a company-style hierarchical multi-agent framework.<n>We find that multi-agent systems organized in a company-style hierarchy generally outperform other organizational structures.
  arXiv  Detail & Related papers  (2026-04-01T15:21:14Z)
• Lemon Agent Technical Report [12.663220335253529]
  Lemon Agent is a multi-agent orchestrator-worker system built on a newly proposed AgentCortex framework.<n>Our system integrates a hierarchical self-adaptive scheduling mechanism that operates at both the overall orchestrator layer and workers layer.<n>By virtue of this two-tier architecture, the system achieves synergistic balance between global task coordination and local task execution.
  arXiv  Detail & Related papers  (2026-02-06T10:09:49Z)
• MegaFlow: Large-Scale Distributed Orchestration System for the Agentic Era [74.42509044145417]
  MegaFlow is a large-scale distributed orchestration system that enables efficient scheduling, resource allocation, and fine-grained task management for agent-environment workloads.<n>In our agent training deployments, MegaFlow successfully orchestrates tens of thousands of concurrent agent tasks while maintaining high system stability and achieving efficient resource utilization.
  arXiv  Detail & Related papers  (2026-01-12T13:25:33Z)
• Designing Domain-Specific Agents via Hierarchical Task Abstraction Mechanism [61.01709143437043]
  We introduce a novel agent design framework centered on a Hierarchical Task Abstraction Mechanism (HTAM)<n>Specifically, HTAM moves beyond emulating social roles, instead structuring multi-agent systems into a logical hierarchy that mirrors the intrinsic task-dependency graph of a given domain.<n>We instantiate this framework as EarthAgent, a multi-agent system tailored for complex geospatial analysis.
  arXiv  Detail & Related papers  (2025-11-21T12:25:47Z)
• FinRobot: Generative Business Process AI Agents for Enterprise Resource Planning in Finance [6.494553545846438]
  We present the first AI-native framework for ERP systems, introducing a novel architecture of Generative Business Process AI Agents.<n>The proposed system integrates generative AI with business process modeling and multi-agent orchestration, enabling end-to-end automation.<n>We show that GBPAs achieve up to 40% reduction in processing time, 94% drop in error rate, and improved regulatory compliance.
  arXiv  Detail & Related papers  (2025-06-02T08:22:28Z)
• Agent-Oriented Planning in Multi-Agent Systems [54.429028104022066]
  We propose AOP, a novel framework for agent-oriented planning in multi-agent systems.<n>In this study, we identify three critical design principles of agent-oriented planning, including solvability, completeness, and non-redundancy.<n> Extensive experiments demonstrate the advancement of AOP in solving real-world problems compared to both single-agent systems and existing planning strategies for multi-agent systems.
  arXiv  Detail & Related papers  (2024-10-03T04:07:51Z)

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.