Model Context Protocol Servers as Orchestration Layers for Generative AI Agents in Enterprise Software Delivery
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V7I2P144Keywords:
Model Context Protocol, AI Agents, Enterprise Software Delivery, CI/CD Orchestration, Generative AI, Agentic Systems, Spring Boot, Intelligent Automation, Context-Aware Computing, Software EngineeringAbstract
Model Context Protocol (MCP) is now a standardised interface for interaction between AI agents, opening new avenues for enterprise software delivery automation. To speed up software engineering processes, intelligent automation, continuous integration and continuous delivery (CI/CD), DevOps pipelines, cloud-native architecture, and large language model (LLM) assistants are becoming more prevalent in modern software firms. The crosscutting issues of communication, inconsistent context, and limited interoperability across development, test, deployment, monitoring, and governance environments pose a major challenge to the integration of heterogeneous AI agents. In this paper, authors provide a formal tool for delivering MCP servers as a context-aware orchestration layer in enterprise CI/CD environments. The proposed architecture places MCP servers between generative AI agents and enterprise software systems, facilitating the exchange of customizable context, coordination of dynamic workflows, intelligent decision support, and secure interactions among various delivery stakeholders. The framework builds on concepts from context-aware computing, agent-based software engineering, semantic orchestration, and service-oriented integration to create a common layer of communication for enterprise development ecosystems. The study explores the benefits of providing context persistence, workflow intelligence, automated task delegation, and real-time decision support via the orchestration using MCP, to enhance software delivery efficiency. It integrates the repository management systems, issue tracking systems, testing frameworks, deployment pipelines, monitoring systems, and knowledge repositories into a shared context. The proposed framework is built on structured workflow analysis and shows improvement of the requirement traceability, deployment reliability, development productivity and operational governance. Moreover, MCP servers give centralized context management that decreases unnecessary agent interactions and boosts coordination among independent software engineering agents. The research is conducted on a conceptual and architecture-based approach, based on the principles of enterprise software engineering. Different orchestration scenarios such as automated code generation, intelligent testing, deployment validation, incident response and continuous monitoring are assessed. The results suggest that using orchestration layers built on top of the MCP can greatly improve the collaboration of AI agents with minimal compromise of enterprise security, compliance, and governance requirements. The architecture itself is also designed to be scalable with modular service integration and extensible protocol interfaces. The suggested framework is part of the expanding body of agentic software delivery that provides a common orchestration model that can be used to support future AI-native enterprise systems. The study offers actionable guidance for organisations looking to leverage generative AI technologies in existing software delivery infrastructure, ensuring reliability, interoperability, and operational transparency. The findings indicate that the MCP servers could be used as basic infrastructure elements of the future intelligent software engineering ecosystem.
References
1. Dey, A. K. (2001). Understanding and using context. Personal and ubiquitous computing, 5(1), 4-7.
2. Chen, G., & Kotz, D. (2000). A survey of context-aware mobile computing research.
3. Schilit, B., Adams, N., & Want, R. (1994, December). Context-aware computing applications. In 1994 first workshop on mobile computing systems and applications (pp. 85-90). IEEE.
4. Brown, P. J., Bovey, J. D., & Chen, X. (1997). Context-aware applications: from the laboratory to the marketplace. IEEE personal communications, 4(5), 58-64.
5. Wooldridge, M. (2009). An introduction to multiagent systems. John wiley & sons.
6. Jennings, N. R. (2001). An agent-based approach for building complex software systems. Communications of the ACM, 44(4), 35-41.
7. Putchakayala, R., & Yallavula, R. (2025). The Intelligent Governance Core: A Multi-Layer AI Framework for Predictive Compliance and Autonomous Digital Analytics. International Journal of AI, BigData, Computational and Management Studies, 6(1), 171-179.
8. Yuvaraj, N., & Kumar, M. S. (2021). From Governed Data to Customer Health Signals: Integrating Telemetry with Enterprise Data Quality Controls. International Journal of Emerging Trends in Computer Science and Information Technology, 2(4), 115-125.
9. Yasodhara Srinivas Aluri. (2025). Micro Frontend Architecture for E-commerce Cart and Checkout Systems. Journal of Computational Analysis and Applications (JoCAAA), 34(11), 777–788.
10. Kumar, M. S., & Yuvaraj, N. (2020). Building a Privacy-Aware Customer Data Foundation: A Governance-First Approach to Digital Service Systems. International Journal of Emerging Research in Engineering and Technology, 1(4), 55-68.
11. Putchakayala, R., & Cherukuri, R. (2022). AI-Enabled Policy-Driven Web Governance: A Full-Stack Java Framework for Privacy-Preserving Digital Ecosystems. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(1), 114-123.
12. Kumar, M. S. (2024). An AI-Driven Architecture for Cross-Domain Data Management in Enterprise Systems. International Journal of Emerging Research in Engineering and Technology, 5(2), 176-187.
13. Aluri, Y. S. (2025). Enhancing developer productivity through intelligent documentation retrieval. Journal of Information Systems Engineering and Management, 10(62s), 629–643.
14. Yallavula, R., & Putchakayala, R. (2022). A Data Governance and Analytics-Enhanced Approach to Mitigating Cyber Threats in NoSQL Database Systems. International Journal of Emerging Trends in Computer Science and Information Technology, 3(3), 90-100.
15. Aluri, Y. S. (2024). Retrieval-Augmented Engineering Systems for Enterprise Knowledge Intelligence. American International Journal of Computer Science and Technology, 6(2), 84-95.
16. Yuvaraj, N., & Kumar, M. S. (2025). Agentic AI for Zero-Touch Customer Experience: A Governance-Constrained Framework for Autonomous Service Systems. American International Journal of Computer Science and Technology, 7(2), 100-111.
17. Aluri, Y. S. (2023). Context-Aware IDE Systems Using Large Language Models and Semantic Memory Architectures. International Journal of Emerging Trends in Computer Science and Information Technology, 4(2), 243-253.
18. Yuvaraj, N. (2024). Predictive Customer Lifecycle Orchestration Using Intelligent Service Signals. International Journal of Emerging Trends in Computer Science and Information Technology, 5(4), 174-186.
19. Cherukuri, R., & Putchakayala, R. (2021). Frontend-Driven Metadata Governance: A Full-Stack Architecture for High-Quality Analytics and Privacy Assurance. International Journal of Emerging Research in Engineering and Technology, 2(3), 95-108.
20. Luck, M., McBurney, P., & Preist, C. (2003). Agent technology: enabling next generation computing (a roadmap for agent based computing). AgentLink.
21. Humble, J., & Farley, D. (2010). Continuous delivery: reliable software releases through build, test, and deployment automation. Pearson Education.
22. Dragoni, N., Giallorenzo, S., Lafuente, A. L., Mazzara, M., Montesi, F., Mustafin, R., & Safina, L. (2017). Microservices: yesterday, today, and tomorrow. Present and ulterior software engineering, 195-216.
23. Schmidt, D. C. (2006). Model-driven engineering. Computer-IEEE Computer Society-, 39(2), 25.
24. Knox, S., Meier, P., Yoon, J., & Harou, J. J. (2018). A python framework for multi-agent simulation of networked resource systems. Environmental modelling & software, 103, 16-28.
