Intelligent Workflow Automation in Cloud-Oriented Software Systems Using Large Language Models and Reinforcement Learning
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V6I1P101Keywords:
Large Language Models (LLMs), Reinforcement Learning (RL), Cloud Computing, Workflow Automation, AIOps, Intelligent Orchestration, Adaptive Systems, Natural Language Processing, Software Engineering Automation, Cloud Resource OptimizationAbstract
The high rate of development of cloud computing, artificial intelligence (AI) and automation has transformed how businesses handle workflows within distributed software architectures. Nevertheless, even with the major breakthrough of orchestration and monitoring software, there is a problem of flexibility, scalability, and intelligent decision-making in cloud workflow automation. The combination of Large Language Models (LLMs) and Reinforcement Learning (RL) has become a new paradigm of developing intelligent, adaptive workflow automation systems that can comprehend the complex enterprise environments, optimize resource usage, and minimize human participation. This paper presents an Intelligent Workflow Automation Framework (IWAF) that uses LLMs to get semantic information and RL to get continuous workflow decision optimization. The LLM aspect converts unstructured business logic, user requirements and system documentation into executable activities with natural language comprehension and program synthesis. The RL component adjusts dynamically parameters of workflow, schedules, and resource assignments based on real-time feedback and performance rewards of cloud execution systems. A vast amount of simulations with the AWS Lambda, Kubernetes, and Google Cloud Run environments was used to test the IWAF model. Findings show efficiency in the completion of the tasks up to 38 percent, latency decreases by 26 percent, and resource utilization up in 42 percent, in contrast to the baseline models with the application of a static rule-based automation. The hybrid AI model proposed can fill the gap between interpretability and adaptability and provide the platform of the future generation of self-optimizing software systems. This study is a part of the rising overlap between AI-based DevOps (AIOps), cloud orchestration, and machine intelligence and suggests a generalizable framework that may be utilized across industries, including healthcare, finance, logistics, and manufacturing
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