Serverless AI Agents in the Cloud

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dc.citation.epage120
dc.citation.issue2
dc.citation.journalTitleДосягнення у кіберфізичних системах
dc.citation.spage115
dc.citation.volume9
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.affiliationInstitute of Solid State Physics, University of Latvia
dc.contributor.authorChaplia, Oleh
dc.contributor.authorKlym, Halyna
dc.contributor.authorElsts, Edgars
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2025-11-06T08:48:12Z
dc.date.created2024-02-27
dc.date.issued2024-02-27
dc.description.abstractIntegrating AI agents within serverless architectures offers a modern approach to deploying and executing intelligent applications. Leveraging the advantages of serverless computing, AI agents can dynamically respond to varying workloads without the overhead of managing the underlying infrastructure. This article explores the concept of scalable serverless AI agents in the cloud, detailing their architecture, benefits and drawbacks, challenges, and real-world applications. The paper provides advantages and drawbacks of the serverless approach. Then a proof-of-concept has been developed, deployed and tested. The AI agent code was deployed to Azure Functions, Google Cloud Functions, and AWS Lambda and tested. As a result, improvements to availability, resilience, reliability, and scalability qualities have been proposed to mitigate the previously defined drawbacks.
dc.format.extent115-120
dc.format.pages6
dc.identifier.citationChaplia O. Serverless AI Agents in the Cloud / Oleh Chaplia, Halyna Klym, Edgars Elsts // Advances in Cyber-Physical Systems. — Lviv : Lviv Politechnic Publishing House, 2024. — Vol 9. — No 2. — P. 115–120.
dc.identifier.citationenChaplia O. Serverless AI Agents in the Cloud / Oleh Chaplia, Halyna Klym, Edgars Elsts // Advances in Cyber-Physical Systems. — Lviv : Lviv Politechnic Publishing House, 2024. — Vol 9. — No 2. — P. 115–120.
dc.identifier.doidoi.org/10.23939/acps2024.02.115
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/117384
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofДосягнення у кіберфізичних системах, 2 (9), 2024
dc.relation.ispartofAdvances in Cyber-Physical Systems, 2 (9), 2024
dc.relation.references[1] Serôdio, C., Mestre, P., Cabral, J., Gomes, M., & Branco, F. (2024). Software and Architecture Orchestration for Process Control in Industry 4.0 Enabled by Cyber-Physical Systems Technologies. Applied Sciences, 14(5), Article 5. DOI: 10.3390/app14052160
dc.relation.references[2] Guldner, A., et al. (2023). A framework for AI-based self-adaptive cyber-physical process systems. Information Technology, 65(3), 113–128. DOI: 10.1515/itit-2023-0001
dc.relation.references[3] Goel, S. (2024). Towards building Autonomous AI Agents and Robots for Open World Environments. New Zealand.
dc.relation.references[4] Chaplia, O., Klym, H., & Popov, A. I. (2024). An Approach to Improving Availability of Microservices for Cyber-Physical Systems. ACPS, 9(1), 16–23. DOI: 10.23939/acps2024.01.016
dc.relation.references[5] Raith, P., Nastic, S., & Dustdar, S. (2023). Serverless Edge Computing – Where We Are and What Lies Ahead. IEEE Internet Computing, 27(3), 50–64. DOI: 10.1109/MIC.2023.3260939
dc.relation.references[6] 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. arXiv. DOI: 10.48550/arXiv.2304.03442
dc.relation.references[7] Wu, Q., et al. (2023). AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation. arXiv. DOI: 10.48550/arXiv.2308.08155
dc.relation.references[8] Kaur, N., & Mittal, A. (2021). Fog Computing Serverless Architecture for Real-Time Unpredictable Traffic. IOP Conference Series: Materials Science and Engineering, 1022(1), 012026. DOI: 10.1088/1757-899X/1022/1/012026
dc.relation.references[9] Aslanpour, M. S., Toosi, A. N., Cheema, M. A., Chhetri, M. B., & Salehi, M. A. (2024). Load balancing for heterogeneous serverless edge computing: A performance-driven and empirical approach. Future Generation Computer Systems, 154, 266-280. DOI: https://doi.org/10.1016/j.future.2024.01.020
dc.relation.references[10] Liu, Z., Zhang, Y., Li, P., Liu, Y., & Yang, D. (2023). Dynamic LLM-Agent Network: An LLM-agent Collaboration Framework with Agent Team Optimization. arXiv. DOI: 10.48550/arXiv.2310.02170
dc.relation.references[11] Al-Doghman, F., Moustafa, N., Khalil, I., Sohrabi, N., Tari, Z., & Zomaya, A. Y. (2023). AI-Enabled Secure Microservices in Edge Computing: Opportunities and Challenges. IEEE Transactions on Services Computing, 16(2), 1485-1504. DOI: https://doi.org/10.1109/TSC.2022.3155447
dc.relation.references[12] Kallas, K., Zhang, H., Alur, R., Angel, S., & Liu, V. (2023). Executing Microservice Applications on Serverless, Correctly. Proceedings of the ACM on Programming Languages, 7. DOI: 10.1145/3571206
dc.relation.references[13] Merlino, G., Tricomi, G., D’Agati, L., Benomar, Z., Longo, F., & Puliafito, A. (2024). FaaS for IoT: Evolving Serverless towards Deviceless in I/Oclouds. Future Generation Computer Systems, 154, 189–205. DOI: 10.1016/j.future.2023.12.029
dc.relation.references[14] Jagutis, M., Russell, S., & Collier, R. (2023). Flexible simulation of traffic with microservices, agents & REST. International Journal of Parallel, Emergent and Distributed Systems, 38(6). DOI: 10.1080/17445760.2023.2242183
dc.relation.references[15] Crawford, N., et al. (2024). BMW Agents – A Framework for Task Automation Through Multi-Agent Collaboration. DOI: 10.48550/ARXIV.2406.20041
dc.relation.references[16] Liu, Z., Yu, H., Fan, G., & Chen, L. (2022). Reliability modelling and optimization for microservice-based cloud application using multi-agent system. IET Communications, 16(10). DOI: 10.1049/cmu2.12371
dc.relation.referencesen[1] Serôdio, C., Mestre, P., Cabral, J., Gomes, M., & Branco, F. (2024). Software and Architecture Orchestration for Process Control in Industry 4.0 Enabled by Cyber-Physical Systems Technologies. Applied Sciences, 14(5), Article 5. DOI: 10.3390/app14052160
dc.relation.referencesen[2] Guldner, A., et al. (2023). A framework for AI-based self-adaptive cyber-physical process systems. Information Technology, 65(3), 113–128. DOI: 10.1515/itit-2023-0001
dc.relation.referencesen[3] Goel, S. (2024). Towards building Autonomous AI Agents and Robots for Open World Environments. New Zealand.
dc.relation.referencesen[4] Chaplia, O., Klym, H., & Popov, A. I. (2024). An Approach to Improving Availability of Microservices for Cyber-Physical Systems. ACPS, 9(1), 16–23. DOI: 10.23939/acps2024.01.016
dc.relation.referencesen[5] Raith, P., Nastic, S., & Dustdar, S. (2023). Serverless Edge Computing – Where We Are and What Lies Ahead. IEEE Internet Computing, 27(3), 50–64. DOI: 10.1109/MIC.2023.3260939
dc.relation.referencesen[6] 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. arXiv. DOI: 10.48550/arXiv.2304.03442
dc.relation.referencesen[7] Wu, Q., et al. (2023). AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation. arXiv. DOI: 10.48550/arXiv.2308.08155
dc.relation.referencesen[8] Kaur, N., & Mittal, A. (2021). Fog Computing Serverless Architecture for Real-Time Unpredictable Traffic. IOP Conference Series: Materials Science and Engineering, 1022(1), 012026. DOI: 10.1088/1757-899X/1022/1/012026
dc.relation.referencesen[9] Aslanpour, M. S., Toosi, A. N., Cheema, M. A., Chhetri, M. B., & Salehi, M. A. (2024). Load balancing for heterogeneous serverless edge computing: A performance-driven and empirical approach. Future Generation Computer Systems, 154, 266-280. DOI: https://doi.org/10.1016/j.future.2024.01.020
dc.relation.referencesen[10] Liu, Z., Zhang, Y., Li, P., Liu, Y., & Yang, D. (2023). Dynamic LLM-Agent Network: An LLM-agent Collaboration Framework with Agent Team Optimization. arXiv. DOI: 10.48550/arXiv.2310.02170
dc.relation.referencesen[11] Al-Doghman, F., Moustafa, N., Khalil, I., Sohrabi, N., Tari, Z., & Zomaya, A. Y. (2023). AI-Enabled Secure Microservices in Edge Computing: Opportunities and Challenges. IEEE Transactions on Services Computing, 16(2), 1485-1504. DOI: https://doi.org/10.1109/TSC.2022.3155447
dc.relation.referencesen[12] Kallas, K., Zhang, H., Alur, R., Angel, S., & Liu, V. (2023). Executing Microservice Applications on Serverless, Correctly. Proceedings of the ACM on Programming Languages, 7. DOI: 10.1145/3571206
dc.relation.referencesen[13] Merlino, G., Tricomi, G., D’Agati, L., Benomar, Z., Longo, F., & Puliafito, A. (2024). FaaS for IoT: Evolving Serverless towards Deviceless in I/Oclouds. Future Generation Computer Systems, 154, 189–205. DOI: 10.1016/j.future.2023.12.029
dc.relation.referencesen[14] Jagutis, M., Russell, S., & Collier, R. (2023). Flexible simulation of traffic with microservices, agents & REST. International Journal of Parallel, Emergent and Distributed Systems, 38(6). DOI: 10.1080/17445760.2023.2242183
dc.relation.referencesen[15] Crawford, N., et al. (2024). BMW Agents – A Framework for Task Automation Through Multi-Agent Collaboration. DOI: 10.48550/ARXIV.2406.20041
dc.relation.referencesen[16] Liu, Z., Yu, H., Fan, G., & Chen, L. (2022). Reliability modelling and optimization for microservice-based cloud application using multi-agent system. IET Communications, 16(10). DOI: 10.1049/cmu2.12371
dc.relation.urihttps://doi.org/10.1016/j.future.2024.01.020
dc.relation.urihttps://doi.org/10.1109/TSC.2022.3155447
dc.rights.holder© Національний університет “Львівська політехніка”, 2024
dc.rights.holder© Chaplia O., Klym H., Elsts E., 2024
dc.subjectCloud computing
dc.subjectCyber-physical systems
dc.subjectAI agents
dc.subjectMicroservices
dc.subjectServerless
dc.titleServerless AI Agents in the Cloud
dc.typeArticle

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Advances In Cyber-Physical Systems. – 2024. – Vol. 9, No. 2