Adaptive AI Agents: Design and Implementation of a Multi-Agent Framework for Automation

  • Unique Paper ID: 194472
  • Volume: 12
  • Issue: 10
  • PageNo: 4228-4234
  • Abstract:
  • The increased need for intelligent automation and informed decision has emphasized the need for intelligent task orchestration and automation, which has traditionally required programming skills. To mitigate these issues, this project proposes a No-Code Multi-Agent Framework for Intelligent Task Orchestration and Automation. This framework provides an opportunity for users to create and implement intelligent multi-agent workflows without programming, using a YAML-based agent definition schema. It represents intelligent agents in terms of modular tasks, arranged in a directed acyclic graph, enabling sequential and parallel execution. It has integrated essential features, including task execution, embedding, vector databases, and logging, making it suitable for intelligent tasks like retrieval-augmented generation, multi-step automation, and knowledge-based tasks. It has also integrated features that enable parallel task execution, output merging, and scalability, making it suitable for cloud and real-time deployment. This framework bridges the gap between research models and practical automation, making it an adaptable, scalable, and user-friendly platform for intelligent automation and orchestration.

Copyright & License

Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{194472,
        author = {Mandala Bhargavi and Dr.B.V. Ramana Murthy and Tadikonda Ramya Sree and A.Thanusree},
        title = {Adaptive AI Agents: Design and Implementation of a Multi-Agent Framework for Automation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {4228-4234},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194472},
        abstract = {The increased need for intelligent automation and informed decision has emphasized the need for intelligent task orchestration and automation, which has traditionally required programming skills. To mitigate these issues, this project proposes a No-Code Multi-Agent Framework for Intelligent Task Orchestration and Automation. This framework provides an opportunity for users to create and implement intelligent multi-agent workflows without programming, using a YAML-based agent definition schema. It represents intelligent agents in terms of modular tasks, arranged in a directed acyclic graph, enabling sequential and parallel execution. It has integrated essential features, including task execution, embedding, vector databases, and logging, making it suitable for intelligent tasks like retrieval-augmented generation, multi-step automation, and knowledge-based tasks. It has also integrated features that enable parallel task execution, output merging, and scalability, making it suitable for cloud and real-time deployment. This framework bridges the gap between research models and practical automation, making it an adaptable, scalable, and user-friendly platform for intelligent automation and orchestration.},
        keywords = {No-code automation; Multi-agent systems; Task orchestration; Directed Acyclic Graph (DAG); YAML schema; Parallel task execution; Retrieval-Augmented Generation (RAG); AI workflow automation; Scalable architectures; Intelligent systems; Workflow automation framework.},
        month = {March},
        }

Cite This Article

Bhargavi, M., & Murthy, D. R., & Sree, T. R., & A.Thanusree, (2026). Adaptive AI Agents: Design and Implementation of a Multi-Agent Framework for Automation. International Journal of Innovative Research in Technology (IJIRT), 12(10), 4228–4234.

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