Citation:

Sarath Vankamardhi Nirmala Varadhi, 2026. "Agentic Orchestration of Generative AI in bulk Workflows", ESP Journal of Engineering & Technology Advancements  6(2): 87-95.

Abstract:

Generative artificial intelligence (AI) is evolving at an alarming rate, and it has opened up some transformative opportunities to automate complex and large-scale workflows.. Specifically, the advent of agentic orchestration, in which several AI agents collaborate, reason, and engage with external tools, has re-established the design and execution of bulk processes. This review discussed the principles, designs, and practice of agentic orchestration in generative AI, particularly in its use to improve scalability, efficiency and reliability in workflows of scale. The article has discussed the main advances in large language models (LLMs), reasoning systems, integration of tools and the collaboration of multiple agents, and how these elements interact with each other to create intelligent workflow automation. The paper through systematic analysis, block diagrams, and a proposed theory showed the superiority of agentic systems over the conventional single-model and pipeline-based systems, especially in the level of accuracy, scalability, and minimization of errors. Experimental evidence suggested the advantages of multi-agent orchestration such as task decomposition, parallelization and iterative testing. Nevertheless, a few unavoidable obstacles were also mentioned by the review including the complexity of the system, the overhead of the coordination, the problems of reliability and even the ethical issues.

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Keywords:

Generative AI, Agentic Orchestration, Large Language Models (LLMs), Multi-Agent Systems, Workflow Automation, Bulk Processing, Task Decomposition, Tool Integration, AI Agents