AutoInfraOps: Infrastructure Deployment via Multi-Step Prompt Pipelines

Abstract

Cloud-native software delivery increasingly depends on DevOps pipelines, Infrastructure-as-Code (IaC), container or-chestration, and continuous integration and deployment (CI/CD) workflows. However, many conventional deployment processes remain static, script-driven, and highly dependent on manual configuration. Such approaches provide repeatability but of-fer limited contextual reasoning, weak adaptation to changing infrastructure requirements, and insufficient feedback-driven optimization. This paper presents AutoInfraOps, a simulation-safe intelligent infrastructure deployment framework based on multi-step prompt pipelines. The proposed framework converts high-level natural language deployment requests into struc-tured requirements, enriched prompts, LLM-assisted reasoning outputs, validated deployment plans, simulated execution logs, monitoring metrics, and feedback suggestions. AutoInfraOps integrates prompt generation, context enrichment, validation and governance, execution simulation, deployment history, and feedback refinement into a unified workflow. The prototype is implemented as a full-stack web application with a React/Vite frontend, Python backend services, SQLite storage, a mock LLM reasoning module, validation services, and a safe execu-tion simulator. Experimental evaluation using four deployment scenarios produced three successful simulations, one validation-blocked deployment, a 75% success rate, 75% prompt efficiency, 65.08 seconds average deployment time, 52.88% average resource utilization, and $31.44 average cost estimate. The results demon-strate that multi-step prompt pipelines can improve transparency, safety, and performance awareness in LLM-assisted DevOps automation while avoiding direct execution of real infrastructure commands.

Introduction

Modern software deployment increasingly relies on cloud-native technologies, microservices, DevOps, CI/CD pipelines, and Infrastructure-as-Code (IaC) tools such as Docker, Kubernetes, Terraform, Ansible, Jenkins, GitHub Actions, and GitLab CI. While these tools improve automation, consistency, and deployment speed, they remain largely static and require manual configuration, making them less effective in dynamic cloud and hybrid environments.

To address these limitations, the paper proposes AutoInfraOps, a simulation-safe framework that integrates Large Language Models (LLMs) into infrastructure deployment through a multi-step prompt pipeline. Instead of directly executing LLM-generated commands, the framework decomposes deployment into structured stages, including requirement analysis, prompt generation, context enrichment, LLM reasoning, validation, execution simulation, monitoring, deployment history, and feedback refinement. This design ensures safety by validating generated plans before simulated execution and avoiding real infrastructure changes.

The proposed system consists of modular components such as a user interface, deployment request processor, prompt generation, context enrichment, LLM reasoning, validation and governance, execution simulation, monitoring, feedback refinement, and database management. A deterministic validation layer checks syntax, resource allocation, security policies, and hallucination risks, blocking unsafe deployment plans before execution.

A full-stack prototype was developed using React.js, Python REST services, SQLite, and a mock LLM engine. The execution engine simulates deployment activities without running actual Docker, Kubernetes, Terraform, or cloud commands. The prototype was evaluated using four deployment scenarios across development, staging, and production environments. Results showed that three deployments completed successfully while one unsafe deployment was correctly blocked by the validation module, demonstrating the effectiveness of the governance layer.

The monitoring module generated metrics such as deployment time, resource utilization, cost estimates, success rate, and prompt efficiency, while the feedback module provided recommendations to improve future deployment prompts. Compared with traditional static deployment workflows, AutoInfraOps offers significant advantages through natural language understanding, context-aware reasoning, deterministic validation, simulation-safe execution, continuous monitoring, and feedback-driven refinement.

Conclusion

This paper presented AutoInfraOps, an intelligent infrastructure deployment framework based on multi-step prompt pipelines. The proposed system converts high-level deploy-ment intent into structured prompts, enriched context, LLM-assisted reasoning outputs, validated deployment plans, simulated execution logs, monitoring metrics, and feedback sugges-tions. The framework emphasizes safety by preventing direct execution of real shell, Docker, Kubernetes, Terraform, or cloud commands. The prototype evaluation demonstrated that AutoInfraOps can process deployment requests, maintain deployment history, validate generated plans, simulate execution, compute per-formance metrics, and generate prompt refinement feedback. The system achieved a 75% success rate and 75% prompt efficiency across four deployment scenarios. The validation-blocked deployment demonstrates the importance of gover-nance in LLM-assisted DevOps workflows. Overall, AutoIn-fraOps provides a safe, transparent, and performance-aware foundation for next-generation DevOps automation using LLM reasoning and multi-step prompt pipelines.

References

[1] J. Humble and D. Farley, Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation. Addison-Wesley, 2010. [2] L. Bass, I. Weber, and L. Zhu, DevOps: A Software Architect’s Perspec-tive. Addison-Wesley, 2015. [3] K. Morris, Infrastructure as Code: Managing Servers in the Cloud. O’Reilly Media, 2016. [4] The Kubernetes Authors, “Kubernetes Documentation,” 2024. [Online]. Available: https://kubernetes.io/docs/ [5] HashiCorp, “Terraform Documentation,” 2024. [Online]. Available: https://developer.hashicorp.com/terraform/docs [6] P. Notaro, J. Cardoso, and M. Gerndt, “A survey of AIOps methods for failure management,” ACM Computing Surveys, vol. 54, no. 11, pp. 1–36, 2021. [7] T. Brown et al., “Language models are few-shot learners,” in Advances in Neural Information Processing Systems, vol. 33, pp. 1877–1901, 2020. [8] OpenAI, “GPT-4 technical report,” arXiv preprint arXiv:2303.08774, 2023. [9] J. Wei et al., “Chain-of-thought prompting elicits reasoning in large lan-guage models,” in Advances in Neural Information Processing Systems, vol. 35, pp. 24824–24837, 2022. [10] S. Yao et al., “ReAct: Synergizing reasoning and acting in language models,” in International Conference on Learning Representations, 2023. [11] T. Schick et al., “Toolformer: Language models can teach themselves to use tools,” in Advances in Neural Information Processing Systems, vol. 36, 2023. [12] L. Wang et al., “A survey on large language model based autonomous agents,” Frontiers of Computer Science, vol. 18, no. 6, 2024. [13] M. Chen et al., “Evaluating large language models trained on code,” arXiv preprint arXiv:2107.03374, 2021. [14] A. Madaan et al., “Self-refine: Iterative refinement with self-feedback,” in Advances in Neural Information Processing Systems, 2023. [15] G. Li et al., “CAMEL: Communicative agents for mind exploration of large language model society,” arXiv preprint arXiv:2303.17760, 2023. [16] R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, 2nd ed. MIT Press, 2018.

Copyright

Copyright © 2026 G. Vaishnavi, Mrs. G. Devi. 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.