Agentic ERP is presented, an expert-system architecture that combines role-aligned large-language-model 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.
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.
Cloud ERP platforms have passed through three distinct automation eras. Scripted batch jobs gave way to robotic process automation, and RPA is now giving way to autonomous agentic AI — systems that reason over enterprise data, select tools dynamically, and execute multi-step business workflows without human direction at every step. The shift is not merely a capability upgrade. Agentic systems behave non-deterministically, invoke tools whose scope may exceed what static governance models anticipate, and can produce cascading process consequences in live financial environments. Governance frameworks built for predictive models and rule-based bots were not designed for this. This paper proposes the Agentic ERP Governance Framework (AEGF), a five-dimension instrument designed to guide the responsible deployment of autonomous AI agents in cloud-based industrial management systems. Drawing on Sociotechnical Systems Theory, the Technology-Organisation-Environment framework, and the NIST AI Risk Management Framework, the AEGF addresses Process Suitability, Autonomy Tiering, Governance and Auditability, Organisational Readiness, and Risk and Continuity Management as an integrated governance architecture. An application to accounts payable automation on Oracle ERP Cloud illustrates how the framework operates in a representative industrial management context.
Venkata Ramachandra Karthik Chundi· International journal of com...· 0 citations
UMA, a Unified Multi-Agent Framework for enterprise AI systems, is introduced, designed to support the complete lifecycle of agentic systems, including deployment, orchestration, execution, monitoring, and return-on-investment (ROI) realization.
Umamaheswara Rao Kukkala· International Journal of Inn...· 0 citations
Large language models (LLMs) have evolved from standalone generative systems into agentic AI systems capable of planning, reasoning, tool use, and multi-agent collaboration. Enterprises are increasingly adopting AI agents to automate and orchestrate complex workflows, from IT operations to employee productivity. While early deployments focused on proof-of-concept prototypes, the past year has marked a clear shift toward production-grade enterprise AI agents. This transition has been enabled by a wave of new technologies, including multi-agent orchestration, memory and state management, skill-based and modular agent architectures, and deeper integration with enterprise data and workflow platforms, which together make scalable, reliable agent systems feasible in practice. At the same time, moving agents into production introduces new technical and organizational challenges, such as rigorous evaluation and benchmarking, security and governance, and system design for long-running, autonomous operation. Building on the success of our two prior highly attended editions: ''Agentic AI for Enterprise'' workshop at KDD 2025 and ''Enterprise RAG'' workshop at CIKM 2024, this workshop aims to bring together researchers and practitioners to examine how enterprise AI agents can successfully move from prototypes to production. We focus on three pillars: 1) Agent architectures and systems; 2) Enterprise applications and deployments; 3) Evaluation and governance.
Min Du, Anbang Xu, Jasmine Jaksic et al.· Proceedings of the 32nd ACM...· 0 citations
The analysis indicates that the promise of LLM-based agents for scalable automation, collaborative reasoning, and complex workflow execution comes with significant challenges in long-horizon reliability, evaluation standardization, communication security, cost-efficient orchestration, governance, and the interpretability of emergent multi-agent behavior.
Nada Mohamed, Prasun Chakrabarti, S. Gupta· Journal of Smart Algorithms...· 0 citations
Enterprises are increasingly building agentic AI systems out of reusable skills — modular units that bundle prompts, reasoning strategies, tool integrations, and execution policies, and that get reused across many AI use cases. This pattern speeds up delivery, but it creates a risk that current AI governance frameworks were not designed for. A single privileged skill, reused across dozens of workflows, can quietly accumulate excess privilege, expand the operational blast radius of every workflow it touches, and drift from its original policy boundary. The NIST AI Risk Management Framework, ISO/IEC 42001, MITRE ATLAS, and OWASP's guidance for LLM and agentic applications all treat AI systems as a single object. None of them gives an organization a way to govern reusable skills as the cross-cutting assets they have become.
This paper argues that reusable agent skills should be treated as first-class governed enterprise assets, and that the enterprise agent harness — not the skill, the model, or the use case — must serve as the runtime trust boundary at which a skill's authority is granted. The paper proposes a runtime governance framework built on three constructs. Skill Risk Inheritance is a design-time model for reasoning about how risk flows through the composition of skills, tools, and use cases. Dynamic Capability Projection (DCP) is the runtime mechanism by which the harness grants, on each invocation, only the subset of a skill's declared capabilities authorized for the current use case and principal. Risk-Adaptive Capability Projection (RACP) extends DCP across time: the granted subset widens or narrows as runtime risk signals change. The framework is grounded in the object-capability tradition, modern policy engines such as OPA and Cedar, and Zero Trust architecture. It is validated through a prototype implementation on Open Policy Agent and a graph-based simulation, which together show that DCP reduces the runtime capability surface to 41% of declared scope withholding 59% of potential capabilities per invocation–at a median policy-evaluation overhead of 8.9ms, negligible against LLM inference latency. Critically, inheritance analysis revealed that 93% of simulated use cases operated at higher effective risk than their declared classification, a finding with immediate implications for enterprise AI risk programs.
Sandeep Kumar Anuguthala· International Journal for Sc...· 0 citations
By foregrounding decision intelligence in complex systems, Enactive AI expands the frontier of AI from model capability to system-aware action, opening new possibilities for scalable, governable, and socially valuable AI deployment.
Zuo-Jun Max Shen, Yuan Qu, Pu-Jun Zhang et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
