Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· 0 citations
TL;DR
This workshop aims to bring together researchers and practitioners to examine how enterprise AI agents can successfully move from prototypes to production, and focuses on three pillars: 1) Agent architectures and systems; 2) Enterprise applications and deployments; 3) Evaluation and governance.
Abstract
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.
Recent advances in large language models (LLMs) have enabled the development of increasingly sophisticated agentic AI systems capable of reasoning, planning, tool use, memory management, and collaborative problem solving. Agentic AI refers to AI systems that can autonomously pursue goals and execute tasks by combining reasoning, decision-making, memory, interaction with external tools, and adaptive workflows. Unlike traditional LLM applications that primarily rely on single-prompt interactions or fixed sequential pipelines, agentic AI systems can iteratively analyze problems, coordinate multiple steps, invoke external resources, refine intermediate outputs, and dynamically adapt their behavior in response to evolving context and feedback. Recent developments in agent frameworks and orchestration platforms have accelerated interest in building such systems for applications involving analytics, research assistance, automation, and decision support. Despite rapid advances in LLM capabilities, designing robust and reliable agentic workflows remains challenging. Developers must address issues related to state management, workflow orchestration, tool integration, multi-agent coordination, reasoning reliability, and transparency of decision-making processes. Recent frameworks such as LangGraph offer new opportunities to build stateful, graph-based AI systems that support complex reasoning workflows, adaptive execution paths, and collaborative multi-agent architectures [1]. This hands-on tutorial introduces LangGraph, a framework built on top of LangChain for designing and orchestrating stateful agentic AI workflows [2]. LangGraph enables developers to construct graph-based systems in which nodes represent reasoning steps, tools, memory modules, or specialized agents, while edges define execution flow and state transitions. Compared with traditional linear LLM pipelines, graph-based architecture offers greater flexibility in implementing branching logic, iterative refinement, recovery mechanisms, memory-aware execution, and collaborative multi-agent interactions. The tutorial focuses on practical techniques for designing and implementing modern agentic AI systems that move beyond simple prompt engineering toward structured reasoning workflows capable of autonomous decision making and coordinated execution. Participants will learn how to build multi-step reasoning pipelines, integrate external APIs and computational tools, manage workflow state and conversational memory, and design collaborative systems involving multiple specialized agents with distinct responsibilities. The tutorial also discusses practical considerations in workflow orchestration, reliability, scalability, evaluation, and deployment of agentic AI systems in real-world environments. Through guided coding exercises implemented in Python using LangGraph, LangChain, and modern LLM APIs, attendees will progressively construct increasingly sophisticated systems. Hands-on activities include building tool-using agents for data analysis and information retrieval, implementing collaborative reasoning systems composed of specialized agents, developing self-refinement and reasoning loops, and constructing decision-support agents that integrate qualitative and quantitative information to generate structured recommendations. Participants will also explore methods for designing transparent and human-centered agentic workflows suitable for real-world applications. The tutorial emphasizes practical system design for data science, analytics, research assistance, and decision-support applications. In addition to implementation techniques, the tutorial examines challenges associated with hallucinations, automation bias, evaluating agent behavior, the reliability of reasoning chains, and the responsible deployment of AI systems that autonomously interact with tools and external information sources. The tutorial encourages participants to adopt human-centered AI principles and design practices that promote transparency, accountability, and responsible use of agentic systems. The tutorial combines conceptual discussions, live coding demonstrations, and interactive, hands-on exercises for data scientists, machine learning engineers, AI researchers, and software developers interested in building scalable, practical agentic AI systems. By the end of the tutorial, participants will have implemented a complete multi-agent decision assistant capable of gathering information, coordinating specialized agents, evaluating alternatives, and generating structured recommendations using modern graph-based AI workflows.
Mohammad Amin Kuhail· Proceedings of the 32nd ACM...· 0 citations
A unified, taxonomy-driven, and deployment-oriented survey of agentic AI systems, synthesizing recent advances through a modular reference architecture and a four-dimensional taxonomy that characterizes agents along the axes of autonomy, tool use, collaboration, and safety–governance is presented.
Sparsh Bajoria, Shreyanshu Ranjan, Adhitya M et al.· Cognitive Computation· 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, Pujun Zhang et al.· 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
Through applied case studies in pharmaceutical discovery and financial systems, common design patterns that make agentic systems successful are analyzed, and practical mitigation strategies for failure modes are discussed, such as verification pipelines, fallback mechanisms, and human-in-the-loop supervision.
Grace Hui Yang, P. Venkit, Hooman Sedghamiz et al.· Proceedings of the 32nd ACM...· 0 citations
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.
Zhihao Liu, Tianyu Wang, X. Wang et al.· 0 citations
