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RESEARCH ARTICLE

AI Agents as a Service (AIAaaS): Emerging Architectures for Cloud-Based Autonomous Decision Making

Dr. Prakash Naik, Anitha Kulkarni, Divya Poojary
IJCCCS Journal Cover
International Journal of Cloud Computing and Cyber Security
© 2026 by IJCCCS
VOLUME Volume 1
ISSUE Issue 1
PUBLICATION January 2026
PAGES 34-52
DOI 10.xxxx/ijcccs.v1i1p03
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CITATION

How to Cite This Article

Dr. Prakash Naik, Anitha Kulkarni, Divya Poojary, 2026. "AI Agents as a Service (AIAaaS): Emerging Architectures for Cloud-Based Autonomous Decision Making" , IJCCCS , Volume 1, Issue 1, pp. 34-52.
ARTICLE SUMMARY

Abstract

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Artificial Intelligence (AI) is quickly transitioning from conventional machine learning uses to autonomous systems that can reason, plan, learn and act. But AI Agents as a Service (AIAaaS) are an emerging construct that takes all the transformation and delivers it in a cloud-native service architecture for intelligent agent capabilities. In contrast to typical Software as a Service (SaaS) platforms that offer the planned functions, AIAaaS allows autonomous agents to autonomously execute complicated activities, interface with digital environments and other brokers for support or disciplinary reasons, and continue adapting in response to new conditions. A variety of technologies, including cloud computing, Large Language Models (LLMs), multi-agent systems, distributed intelligence and autonomous decision-making frameworks to realize next-generation intelligent services through cloud-native agent ecosystems are now entering a global scale development phase.AI Agents as a Service This is introducing an entirely new computing model, whereby autonomous agents serve as on-demand, reusable and scalable services capable of being accessed through cloud infrastructures. Agents that can reason based on context, remember information, carry out workflows across different applications, retrieve knowledge from 100s of documents and adapt over time Intelligent agents can be deployed in enterprises, customer experience and engagement, healthcare, finance, cybersecurity, industrial automation etc., smart cities and at different levels of the stack. AIAaaS platforms enable the scaling of autonomous intelligence through elasticity, high availability (HA), distributed processing and easy integration with enterprise systems offered by cloud-native architectures.Linguistic data and agentic reasoning frameworks are at the core of AIAaaS. Current AI agents utilize LLMs for understanding natural language, generating plans, decomposing complex tasks into a series of simpler ones, interfacing with external tools and executing without supervision. It is better at using memory systems, retrieval-augmented generation, knowledge graphs and multi-agent collaboration mechanisms. This enables AI agents to stay aware of the context, manage task execution, and improve through learning from experiences and feedback.

INDEX TERMS

Keywords

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AI Agents as a Service, AIAaaS, Autonomous Intelligence, Cloud-Native AI, Multi-Agent Systems, Large Language Models, Agentic AI; Cloud Computing; Intelligent Services [13] Cloud-Native-AI and Multi-Agent Systems-1.

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