2024· International Journal of Modern Innovations and Emerging Trends· Vol 7, pp. 01-14· 0 citations
TL;DR
This paper explores Agentic AI architectures for autonomous business applications, highlighting their role in finance, healthcare, supply chain, enterprise resource planning, customer relationship management, and industrial operations and their importance for successful enterprise adoption.
Abstract
Agentic Artificial Intelligence (Agentic AI) represents the next generation of intelligent systems capable of autonomous sensing, reasoning, planning, and action with minimal human intervention. Unlike traditional AI, Agentic AI integrates large language models, reinforcement learning, multi-agent systems, planning mechanisms, orchestration layers, and memory modules to enable adaptive and goal-oriented decision-making. This paper explores Agentic AI architectures for autonomous business applications, highlighting their role in finance, healthcare, supply chain, enterprise resource planning, customer relationship management, and industrial operations. A layered architecture comprising perception, reasoning, orchestration, and execution layers is proposed to support autonomous analysis, strategic planning, and optimized action execution. The framework also incorporates memory, monitoring, and governance modules to enhance transparency, reliability, and explainability. Experimental evaluation demonstrates that the proposed architecture improves workflow automation, decision accuracy, operational efficiency, resource utilization, and response time while reducing manual intervention. The findings indicate that Agentic AI provides a scalable and robust foundation for future autonomous enterprise systems. The study also discusses key challenges, including explainability, governance, ethics, and trust, emphasizing their importance for successful enterprise adoption. Overall, Agentic AI architectures offer significant potential to accelerate intelligent automation and drive the next generation of business transformation.
A comprehensive framework for the design, evaluation, and responsible deployment of Agentic AI is proposed, emphasizing safety, explainability, human-in-the-loop supervision, and ethical compliance and aims to maximize the benefits of Agentic AI while minimizing potential risks.
Nitin S. Shrirao, Dnyaneshwar S. Jadhav, Sarita B. Patil· Recent Trends in Mathematics· 0 citations
The evolution of artificial intelligence has enabled autonomous AI agents to reason, plan and make decisions in complex and dynamic environments. AI that behaves human-like is not a single model or a rule-based system. It is a collection of agents that collaborate to find solutions to issues that a single AI model is unable to handle. This paper discusses the development of agentic AI frameworks, their implementation, and specific instances of its use for autonomous multi-agent reasoning, especially for business decision intelligence. We assess the degree to which LLM-powered agents can solve complex issues in a way that appears natural to humans by using tools, gathering knowledge, planning, and interacting with one other. The study also looks at the advantages of these frameworks for business decision-making, such as real-time data analysis, predictive insights, workflow automation, and creating strategies that adapt to changing conditions. The study also talks about how decision intelligence may be utilized in real life in several fields of business, like monitoring the supply chain, making financial predictions, managing customer relationships, and improving operations. Agent coordination, scalability, dependability, explainability, and ethical control are all thoroughly investigated and debated. Additionally, this study offers a conceptual framework that illustrates the interactions between the many components of a multi-agent system, including perception, thinking, planning, and action. The results suggest that AI frameworks that adopt an agentic approach can considerably increase the accuracy, efficiency, and adaptability of business decisions. They also show how important it is to have good governance structures to make sure that business autonomous decision-making is reliable and clear.
Praveen Dommalapati· International Conference Com...· 0 citations
Artificial Intelligence (AI) agents have emerged as a transformative paradigm in modern computing, extending
traditional artificial intelligence systems by enabling autonomous perception, reasoning, planning, learning, and decisionmaking. Recent advances in foundation models, large language models (LLMs), reinforcement learning, and multi-agent
systems have significantly accelerated the adoption of AI agents across healthcare, finance, education, cybersecurity,
manufacturing, transportation, agriculture, and smart cities. This review synthesizes the current state of AI agent research
by examining their conceptual foundations, architectural components, classifications, learning mechanisms, and practical
applications. Furthermore, the paper discusses emerging paradigms including autonomous agents, agentic AI, collaborative
multi-agent systems, and LLM-powered intelligent assistants. Finally, key challenges including explainability,
trustworthiness, safety, privacy, governance, and ethical considerations are critically analyzed, while future research
directions are identified to support the development of robust, scalable, and human-centered AI agent ecosystems.
Njoku Dominic Okechukwu, Ukachukwu Theddius Ndukaku, Okorie Juliet Ijeoma· International Journal of Inn...· 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
Artificial Intelligence (AI), Machine Learning (ML), and autonomous intelligent systems are transforming predictive decision-making across industries such as manufacturing, healthcare, finance, transportation, cybersecurity, and smart cities. Traditional centralized machine learning models often struggle to adapt to dynamic and uncertain environments. This paper proposes an Agent-Based Machine Learning Framework for Autonomous Predictive Decision Systems (ABML-APDS) that integrates distributed intelligent agents, collaborative learning, reinforcement learning, predictive analytics, and explainable AI into a unified architecture. The framework enables autonomous agents to collect data, engineer features, exchange knowledge, optimize predictions, and continuously improve decision-making with minimal human intervention. It incorporates explainable decision mechanisms to enhance transparency, trust, and interpretability while supporting supervised, unsupervised, deep, and reinforcement learning models. Continuous learning and decentralized agent collaboration improve adaptability, scalability, fault tolerance, computational efficiency, and real-time responsiveness. The proposed framework provides an intelligent and scalable foundation for next-generation autonomous predictive systems supporting Industry 5.0, cyber-physical systems, IoT, smart manufacturing, precision healthcare, and AI-driven digital transformation.
Grace Ndlovu, Samuel Johnson· International Journal of Mac...· 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
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