While multiple programming agents are beingput on market but they are currently meant forstandalone tasks, not for engineering softwarethat has numerous moving parts being integrated.Our methodology emphasizes modularity, robust-ness, and traceability. The system begins withprompt validation and task decomposition usingLLMs, followed by graph-based orchestrationof agents. This work presents Flomny, a multi-agentic system that automates software develop-ment workflows and their integration. Integra-tion Agents leverage Pinecone for vector-basedretrieval and produce code grounded in documen-tation rather than relying solely on the LLM’sinternal knowledge. Additionally, Flomny inte-grates the Model Context Protocol (MCP) to en-able advanced agent-based execution capabilities,allowing for dynamic discovery and utilization ofexternal tools while maintaining security bound-aries and user control. Static code validation pre-cedes optional execution in isolated subprocesses,enhancing both safety and reliability. Through adetailed case study involving Custom Servers andDiscord integrations, we demonstrate Flomny’sability to automatically generate and execute afour-step workflow derived entirely from a natu-ral language prompt. The results show successfultask decomposition, accurate API calls, and seam-less execution with real-time updates. By combin-ing the interpretability of LLMs, the structure ofgraph-based multi-agent systems, the precision ofretrieval- augmented code generation, and the flex-ibility of MCP integration, Flomny paves the wayfor more accessible, scalable, and human-centricworkflow automation.