Open AGI - Multi-Agent Automation Framework for AI-Orchestrated Software Development

Abstract

The quick growth of Artificial Intelligence (AI) and Large Language Models (LLMs) has sped up the creation of autonomous systems that can handle complex tasks similar to those performed by humans. Even with these improvements, software development still requires a lot of human effort, teamwork, and industry knowledge. This paper introduces Open AGI, a Multi-Agent Automation Framework designed for AI- managed software development. In this system, several intelligent agents work together to turn natural language requirements into complete software solutions that can be deployed. Each agent in Open AGI has a specific role, such as Product Manager, Architect, Developer, and Quality Analyst, and follows set Standard Operating Procedures (SOPs) to ensure consistency, accuracy, and reliability throughout the development process. The survey reviews current multi-agent and LLM-based frameworks like AutoGPT, LangChain, and MetaGPT, pointing out their strengths and weaknesses in automating software engineering tasks. By comparing these systems, the paper highlights the research gap that Open AGI fills: the lack of a combined, role-based automation framework capable of managing the entire software development process using AI management. This new approach not only simplifies project execution but also encourages scalability, flexibility, and less reliance on human input in software creation.

Introduction

Artificial Intelligence has evolved from simple rule-based systems to advanced Large Language Models (LLMs) such as GPT-4, Claude 3, Gemini 1.5, and LLaMA 3. These models can understand natural language, reason, generate code, and perform complex tasks. Despite these advances, software development still relies heavily on human coordination among specialists such as product managers, architects, developers, testers, and DevOps engineers. This dependence often creates communication bottlenecks, delays, and inconsistencies.

Traditional automation tools have focused on limited tasks like code completion and testing. More recent multi-agent frameworks, including AutoGPT, LangChain, Microsoft AutoGen, and MetaGPT, allow multiple AI agents to collaborate. However, these frameworks often lack persistent memory, structured workflows, lifecycle management, and full deployment capabilities.

To overcome these limitations, Open AGI proposes a unified multi-agent framework that follows Standard Operating Procedures (SOPs) and mirrors the structure of a software company. Specialized AI agents take on roles such as Product Manager, Architect, Developer, QA Engineer, and DevOps Engineer. Through hierarchical communication, persistent memory, and clearly defined responsibilities, Open AGI can autonomously generate requirements, designs, source code, test cases, deployment scripts, and other software artifacts directly from natural-language instructions.

The literature review highlights the progression of AI from symbolic systems and expert systems to machine learning, deep learning, transformers, and multi-agent collaboration. It also identifies key challenges in existing frameworks, including fragmented workflows, lack of long-term memory, weak evaluation methods, limited deployment support, and ethical concerns such as hallucinations and overtrust.

Open AGI addresses these gaps by combining:

• Persistent memory using vector databases.
• SOP-driven workflows and role-based collaboration.
• Integrated DevOps tools such as Git, Docker, and CI/CD pipelines.
• Transparent validation and peer-review mechanisms among agents.
• Human oversight and explainability.

References

[1] Cassio Pennachin and Ben Goertzel, 2007. \"Contemporary Approaches to Artificial General Intelligence\", Artificial general intelligence - Springer Berlin Heidelberg, pp. 1-30. [2] Ben Goertzel, 2023. “Generative AI vs. AGI: The cognitive strengths and weaknesses of modern LLMs”, ArXiv preprint arXiv:2309.10371 [3] Nanyi Fei, Zhiwu Lu, et al, 2022. “Towards artificial general intelligence via a multimodal foundation model”, Nature Communications, 13/1, pp. 3094. [4] Fernando Diaz, and Michael Madaio, 2024. “Scaling laws do not scale”, Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society. 7, pp. 341-357. [5] Qingyun Wu, Gagan Bansal, et al, 2023. “AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation”, arXiv preprint arXiv:2308.08155, ¾ [6] Rahul M. Samant, et al, 2013. \"A Study on Feature Selection Methods in Medical Decision Support Systems\", International Journal of Engineering Research & Technology (IJERT), 2/11, pp. 615-619. [7] Ben Goertzel, “ Artificial General Intelligence : Concept, State of the art, and Future prospects”, Journal of Artificial General Intelligence, 5/1, 2014. [8] J. Grudin and Richard Jacques, 2019. “Chatbots, Humbots, and The Quest for Artificial General Intelligence”, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1-11. [9] Peter Voss, 2007. “Essentials of General Intelligence: The Direct Path to Artificial General Intelligence”, Springer Berlin Heidelberg, pp. 131– 157. [10] Sébastien Bubeck, Varun Chadrasekaran, et al, 2023. “Sparks of Artificial [11] General Intelligence: Early Experiments with GPT-4 ”, ArXiv, 2303. [12] Rahul Samant and Srikantha Rao, 2013. “A study on Comparative Performance of SVM Classifier Models with Kernel Functions in Prediction of Hypertension”, International Journal of Computer Science and Information Technologies, 4/6, pp. 818-821. [13] Minqi Jiang, Tim Rocktäschel, Edward Grefenstette, 2023. “General intelligence requires rethinking exploration”, Royal Society Open Science, 10/6, pp. 230539. [14] Lin Zhao, Lu Zhang, Zihao Wu, Yuzhong Chen, Haixing Dai, Xiaowei [15] Yu, et al, 2023. “ When brain inspired AI meets AGI ”, Meta-Radiology [16] - Elsevier, 1/1, pp. 100005. [17] James Babcock, János Kramár and Roman Yampolskiy, 2016. “The AGI containment problem”, Artificial General Intelligence: 9th International Conference, AGI 2016, New York, NY, USA, July 16-19, 2016, Proceedings 9, Springer International Publishing, pp. 53-63. [18] Andrzej Cichocki and Alexander P Kuleshov, 2021. “Future trends for Human-AI collaboration: A Comprehensive Taxonomy of AI/AGI using Multiple Intelligences and Learning Styles”, Computational Intelligence and Neuroscience - Hindawi, 2021/1, pp. 8893795 [19] Rahul Samant and Srikantha Rao, 2013. “Evaluation of Artificial Neural Networks in Prediction of Essential Hypertension”, International Journal of Computer Applications, 81/12. [20] T. Everitt, G. Lea and M. Hutter, 2018. “ AGI Safety Literature Review ”, International Joint Conference on AI. [21] D. B. Acharya, K. Kuppan and B Divya, 2025. “Agentic AI: Autonomous intelligence for complex goals – A comprehensive survey”, IEEE Access [22] Erik Miehling, Karthikeyan Natesan Ramamurthy, Kush R Varshney, Matthew Riemer, Djallel Bouneffouf, John T Richards, Amit Dhurandhar, et al, 2025. “Agentic AI Needs a Systems Theory”, arXiv preprint arXiv:2503.00237, IBM Research. [23] Shanmugasundaram Sivakumar, 2024. “Agentic AI in Predictive AIOps: Enhancing IT Autonomy and Performance”, International Journal of Scientific Research and Management (IJSRM), 12/11, pp. 1631- 1638. [24] Antonio Lieto, Mehul Bhatt, Alessandro Oltramari, David Vernon, 2018. “The role of cognitive architectures in General Artificial Intelligence”, Cognitive Systems Research, Volume - 48, pp. 1-3. [25] Steve J Bickley and Benno Torgler, 2023. “Cognitive architectures for artificial intelligence ethics”, AI & Society, 38/2, pp. 501-519. [26] Qian Niu, Junyu Liu, et al, 2024. “Large Language Models and Cognitive Science: A Comprehensive Review of Similarities, Differences, and Challenges”, arXiv preprint arXiv:2409.02387. [27] Henry Hexmoor, Johan Lammens, et al, 2025. “Behaviour Based AI, Cognitive Processes, And Emergent Behaviors in Autonomous Agents”, WIT Transactions on Information and Communication Technologies, Volume-1. [28] Teppo Felin, Matthias Holweg, 2024. “Theory Is All You Need: AI, Human Cognition, and Decision Making”, Strategy Science, 9/4, pp. 346- 371.

Copyright

Copyright © 2026 Ms. Dhanashree Patil, Gauri Dongre, Laukik Patil, Shubham Maske. This 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.