Imagine a courtroom where the jury isn't human, but a panel of AI agents engaging in a rigorous debate to judge the quality of a translation. This isn't science fiction; it's the innovative approach behind M-MAD, a new framework poised to revolutionize how we evaluate machine translations (MT). Traditionally, MT evaluation has relied on learned automatic metrics or human assessment, both with their limitations. Learned metrics need massive datasets and often struggle with nuanced judgments, while human evaluations are time-consuming and expensive. Enter M-MAD, or Multidimensional Multi-Agent Debate, which takes a dramatically different approach. This framework uses multiple AI agents, each specializing in a different aspect of translation quality, like accuracy, fluency, or style. These agents don't just offer individual opinions; they engage in a structured debate, challenging each other's assessments and refining their judgments through back-and-forth argumentation. This collaborative process allows the AI agents to reach a more nuanced and accurate consensus than any single agent or traditional metric could achieve. In tests using the WMT 2023 Metrics Shared Task dataset, M-MAD outperformed existing LLM-as-a-judge methods and even rivaled some of the most advanced automatic metrics. Surprisingly, even with a less powerful LLM like GPT-4o mini, M-MAD delivered competitive results, suggesting its potential to democratize high-quality MT evaluation. While M-MAD shows immense promise, there are still challenges to overcome. The computational cost, especially with more advanced LLMs, remains significant, and refining the debating strategies for optimal performance is an ongoing area of research. Moreover, as translation quality improves, even human annotations become less reliable, underscoring the need for new evaluation paradigms. M-MAD represents a significant leap towards a future where AI not only translates languages but also judges the quality of those translations with human-like precision and depth. This framework not only pushes the boundaries of MT evaluation but also opens up exciting new avenues for research in multi-agent collaboration and AI reasoning.