Open Access

Engineering Autonomous Multi-Agent Software Systems: Implementing Hybrid Architectures, Interaction Protocols, and Execution Loops

4 Full-Stack Engineer Kyiv, Ukraine

Abstract

The paper examines the engineering transition from static software paradigms to autonomous agentic architectures (Software Engineering 3.0). Instead of focusing on organizational theory, the analysis concentrates on the technical implementation of functional subject attributes in software agents: goal planning, causal reasoning, and standardized execution. Key architectural patterns for deploying distributed multi-agent systems (MAS) are synthesized, specifically focusing on the integration of generative models with Case-Based Reasoning for strictly typed decision reproducibility. The study details the technical requirements for Agent-to-Agent (A2A) communication protocols and the establishment of stable behavioral contracts between autonomous entities. Implementation challenges are addressed through the lens of data engineering, specifically context memory management and vector data integration within existing IT landscapes. Furthermore, the paper structures the technical aspects of Artificial Intelligence Trust, Risk, and Security Management (AI TRiSM), defining methods for behavioral control, immutable logging, and decision traceability in high-load environments. The efficiency analysis is reframed from general business ROI to specific engineering metrics, correlating system performance with the costs of development, integration, and computational maintenance.

How to Cite

Nykoliuk, M. (2024). Engineering Autonomous Multi-Agent Software Systems: Implementing Hybrid Architectures, Interaction Protocols, and Execution Loops. Frontiers in Emerging Computer Science and Information Technology, 1(2), 75–85. Retrieved from https://irjernet.com/index.php/fecsit/article/view/368

References

📄 McKinsey & Company. (2024, May 30). The state of AI in early 2024: Gen AI adoption spikes and starts to generate value. Retrieved from: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-2024 (date accessed: June 6, 2024).
📄 Stanford University Human-Centered Artificial Intelligence. (2024). Artificial Intelligence Index Report 2024. Retrieved from: https://hai.stanford.edu/ai-index/2024-ai-index-report (date accessed: June 10, 2024).
📄 Deloitte. (2024). The state of generative AI in the enterprise: Q1 report. Retrieved from: https://www.deloitte.com/ce/en/services/consulting/research/state-of-generative-ai-in-enterprise.html (date accessed: June 14, 2024).
📄 Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2024). Generative AI. Business & Information Systems Engineering, 66(1), 111–126. https://doi.org/10.1007/s12599-023-00834-7
📄 Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., et al. (2023). The rise and potential of large language model based agents: A survey. arXiv. https://doi.org/10.48550/arXiv.2309.07864
📄 Kanbach, D. K., Heiduk, L., Blueher, G., Schreiter, M., & Lahmann, A. (2024). The GenAI is out of the bottle: Generative artificial intelligence from a business model innovation perspective. Review of Managerial Science, 18(4), 1189–1220. https://doi.org/10.1007/s11846-023-00696-z
📄 Benaich, N., & Hogarth, I. (2023). State of AI Report 2023. Retrieved from: https://www.stateof.ai/2023 (date accessed: March 18, 2024).
📄 OpenAI. (2024, January 10). Introducing ChatGPT Team. Retrieved from: https://openai.com/index/introducing-chatgpt-team/ (date accessed: January 18, 2024).
📄 Wu, Q., Bansal, G., Zhang, J., Wu, Y., Li, B., Zhu, E., Jiang, L., Zhang, X., Zhang, S., Liu, J., Awadallah, A. H., White, R. W., Burger, D., & Wang, C. (2023). AutoGen: Enabling next-gen LLM applications via multi-agent conversation. arXiv. https://doi.org/10.48550/arXiv.2308.08155
📄 Chen, W., Su, Y., Zuo, J., Yang, C., Yuan, C., Chan, C.-M., et al. (2023). AgentVerse: Facilitating multi-agent collaboration and exploring emergent behaviors. arXiv. https://doi.org/10.48550/arXiv.2308.10848
📄 Guo, T., Chen, X., Wang, Y., Chang, R., Pei, S., Chawla, N. V., Wiest, O., & Zhang, X. (2024). Large language model based multi-agents: A survey of progress and challenges. arXiv. https://doi.org/10.48550/arXiv.2402.01680
📄 Li, G., Hammoud, H. A. A. K., Itani, H., Khizbullin, D., & Ghanem, B. (2023). CAMEL: Communicative agents for “mind” exploration of large language model society. arXiv. https://doi.org/10.48550/arXiv.2303.17760
📄 Hong, S., Zhuge, M., Chen, J., Zheng, X., Cheng, Y., Zhang, C., et al. (2023). MetaGPT: Meta programming for a multi-agent collaborative framework. arXiv. https://doi.org/10.48550/arXiv.2308.00352
📄 Microsoft. (2024, June 24). Introduction to Semantic Kernel. Retrieved from: https://learn.microsoft.com/en-us/semantic-kernel/overview/ (date accessed: June 28, 2024).
📄 Figma. (n.d.). Figma AI. Retrieved from: https://www.figma.com/ai/ (date accessed: July 2, 2024).
📄 Supabase. (n.d.). Architecture. Retrieved from: https://supabase.com/docs/guides/getting-started/architecture (date accessed: February 7, 2024).
📄 Supabase. (n.d.). Auth architecture. Retrieved from: https://supabase.com/docs/guides/auth/architecture (date accessed: February 12, 2024).
📄 TypeScript. (n.d.). The TypeScript Handbook. Retrieved from: https://www.typescriptlang.org/docs/handbook/intro.html (date accessed: March 5, 2024).
📄 Ant Design. (n.d.). Components overview. Retrieved from: https://ant.design/components/overview/ (date accessed: April 16, 2024).
📄 OWASP Foundation. (2024). Top 10 for LLMs and Gen AI Apps 2023–24. Retrieved from: https://genai.owasp.org/llm-top-10-2023-24/ (date accessed: April 18, 2024).
📄 Vandevenne, N., Van Riel, J., & Poels, G. (2023). Green enterprise architecture (GREAN): Leveraging EA for environmentally sustainable digital transformation. Sustainability, 15(19), 14342. https://doi.org/10.3390/su151914342
📄 van de Wetering, R. (2022). The role of enterprise architecture-driven dynamic capabilities and operational digital ambidexterity in driving business value under the COVID-19 shock. Heliyon, 8(11), e11484. https://doi.org/10.1016/j.heliyon.2022.e11484
📄 Park, J. S., O’Brien, J. C., Cai, C. J., Morris, M. R., Liang, P., & Bernstein, M. S. (2023). Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology. https://doi.org/10.1145/3586183.3606763
📄 Wang, G., Xie, Y., Jiang, Y., Mandlekar, A., Xiao, C., Zhu, Y., Fan, L., & Anandkumar, A. (2023). Voyager: An open-ended embodied agent with large language models. arXiv. https://doi.org/10.48550/arXiv.2305.16291
📄 Shinn, N., Cassano, F., Berman, E., Gopinath, A., Narasimhan, K., & Yao, S. (2023). Reflexion: Language agents with verbal reinforcement learning. arXiv. https://doi.org/10.48550/arXiv.2303.11366
📄 Du, Y., Li, S., Torralba, A., Tenenbaum, J. B., & Mordatch, I. (2023). Improving factuality and reasoning in language models through multiagent debate. arXiv. https://doi.org/10.48550/arXiv.2305.14325
📄 Weisz, J. D., He, J., Muller, M., Hoefer, G., Miles, R., & Geyer, W. (2024). Design principles for generative AI applications. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3613904.3642466
📄 Chaudhry, B. M. (2024). Concerns and challenges of AI tools in the UI/UX design process: A cross-sectional survey. In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3613905.3650878
📄 Qian, C., Liu, W., Liu, H., Chen, N., Dang, Y., Li, J., Yang, C., Chen, W., Su, Y., Cong, X., Xu, J., Li, D., Liu, Z., & Sun, M. (2023). ChatDev: Communicative agents for software development. arXiv. https://doi.org/10.48550/arXiv.2307.07924
📄 Yang, J., Jimenez, C. E., Wettig, A., Lieret, K., Yao, S., Narasimhan, K., & Press, O. (2024). SWE-agent: Agent-computer interfaces enable automated software engineering. arXiv. https://doi.org/10.48550/arXiv.2405.15793
📄 Huang, D., Zhang, J. M., Luck, M., Bu, Q., Qing, Y., & Cui, H. (2023). AgentCoder: Multi-agent-based code generation with iterative testing and optimisation. arXiv. https://doi.org/10.48550/arXiv.2312.13010
📄 Nielsen Norman Group. (2023, January 29). CASTLE framework for productivity/workplace applications. Retrieved from: https://www.nngroup.com/articles/castle-framework/ (date accessed: January 31, 2024).
📄 Nielsen Norman Group. (1994, April 24). 10 usability heuristics for user interface design. Retrieved from: https://www.nngroup.com/articles/ten-usability-heuristics/ (date accessed: February 2, 2024).
📄 Figma. (n.d.). Anticipation, experimentation and AI: Design trend report. Retrieved from: https://www.figma.com/reports/ai-design-trends-2024/ (date accessed: July 5, 2024).
📄 McKinsey Global Institute. (2023, June 14). The economic potential of generative AI: The next productivity frontier. Retrieved from: https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier (date accessed: May 6, 2024).
📄 Wang, L., Ma, C., Feng, X., Zhang, Z., Yang, H., Zhang, J., et al. (2024). A survey on large language model based autonomous agents. Frontiers of Computer Science, 18, 186345. https://doi.org/10.1007/s11704-024-40231-1
📄 Kar, A. K., Varsha, P. S., & Rajan, S. (2023). Unravelling the impact of generative artificial intelligence (GAI) in industrial applications: A review of scientific and grey literature. Global Journal of Flexible Systems Management, 24(4), 659–689. https://doi.org/10.1007/s40171-023-00356-x
📄 Microsoft Research. (n.d.). AutoGen. Retrieved from: https://www.microsoft.com/en-us/research/project/autogen/(date accessed: June 20, 2024).
📄 Singh, K., Chatterjee, S., & Mariani, M. (2024). Applications of generative AI and future organizational performance: The mediating role of explorative and exploitative innovation and the moderating role of ethical dilemmas and environmental dynamism. Technovation, 133, 103021. https://doi.org/10.1016/j.technovation.2024.103021
📄 OpenAI. (2024, January 10). ChatGPT release notes. Retrieved from: https://help.openai.com/en/articles/6825453-chatgpt-release-notes (date accessed: January 20, 2024).
📄 National Institute of Standards and Technology. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0) [PDF]. Retrieved from: https://nvlpubs.nist.gov/nistpubs/ai/nist.ai.100-1.pdf (date accessed: February 20, 2024).