DevOps AI: Intelligent Multi-Agent Platform

Modern DevOps pipelines rely on a chain of specialised tools for version control, containerisation, deployment, monitoring, documentation and database administration. Each tool automates its own task, yet engineers still move between interfaces, remember tool-specific commands and stitch the workflow together by hand, which raises the risk of configuration mistakes and slows troubleshooting. This paper presents the design of a multi-agent DevOps platform in which a locally hosted large language model acts as a central orchestrator. A user submits a request in plain language through a web dashboard; the orchestrator interprets the intent, selects one of five specialised agents (Deployment, Docker, Monitoring, Documentation or Database), invokes the relevant tool through a restricted execution layer, and returns the outcome with an explanation. The platform is built on Ollama, LangGraph, Node.js, React, Docker, MongoDB, Redis, Prometheus and Grafana. Running the model locally keeps project data and execution context inside the organisation’s own infrastructure. Operations that can change or destroy state, such as production deployment, rollback, container removal or database deletion, pass through a risk-evaluation step and require explicit human approval, and every action is written to an audit log. We report the Phase-I outcomes, namely the architecture, agent responsibilities, risk policy and workflow, and define an evaluation protocol covering task accuracy, deployment success, execution time, monitoring detection, documentation quality, recovery rate, resource use and human intervention. Quantitative results will follow the prototype evaluation. The work offers a reusable, extensible and locally deployable blueprint for conversational DevOps automation.