Agentic AI applications increasingly rely on multiple agents with distinct roles, specialized tools, and access to memory layers to solve complex tasks -- closely resembling service-oriented architectures. Yet, in the rapid evolving landscape of programming frameworks and new protocols, deploying and testing AI agents as distributed systems remains a daunting and labor-intensive task. We present DMAS-Forge, a framework designed to close this gap. DMAS-Forge decouples application logic from specific deployment choices, and aims at transparently generating the necessary glue code and configurations to spawn distributed multi-agent applications across diverse deployment scenarios with minimal manual effort. We present our vision, design principles, and a prototype of DMAS-Forge. Finally, we discuss the opportunities and future work for our approach.