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@article{192211,
        author = {Venkata Sai Sandeep Velaga and Annu Mishra},
        title = {A Comprehensive Framework for Enterprise Automation Using Generative AI Agents},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {673-679},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192211},
        abstract = {The latest developments in foundation models on the large scale have made it possible to have autonomous computational agents that can reason, plan, use tools, and execute tasks in an iterative manner. These Generative Artificial Intelligence (GenAI) agents are reimagining enterprise automation by not being limited to rule-based system automation but creating adaptive, cognitive automation. In this paper, a detailed GenAI-based Agent Frameworks are suggested which will automate sophisticated multi-step activities in the enterprise that used to be involved in human judgment, contextual reasoning, and dynamic decision making. The architecture proposed incorporates prompt driven agent cognition, hierarchical planning structures, structured long term memory, constraints in accordance with policies, tool and action execution layers, and self-verification by the provision of iterative refinement loops.
The multi agent coordination model is presented to allow task breakdown, concurrent execution, dependency solving and recovery of failures in the large-scale workflow of an enterprise. Formal algorithms of agent planning, retrieval of memory, coordination and verification are provided. The overall performance benchmarking of the representative enterprise automation activities such as IT activities, business process automation, customer services, and data analysis show considerable reduction in the time spent on the task completion, implementation consistency, and accuracy compared to the traditional scripted automation and rule-of-thumb systems. Other essential enterprise design factors considered in the study include safety, reliability, controllability, transparency and auditability. Lastly, directions in future research are described, such as self-learning agents, interoperability standards of agents across vendors, reinforcement-based optimization, and standard evaluation benchmarks. The results place GenAI agents as the base towards intelligent automation of enterprises in the future.},
        keywords = {Generative AI, Autonomous Agents, Enterprise Automation, Multi-Agent Systems, Large Language Models, AI Planning, Observability, Intelligent Workflows},
        month = {February},
        }