We present TartanMaroon, a deployable multi-agent academic advising system that handles the full complexity spectrum of student queries, from factual lookups to constrained multi-semester planning. We make three contributions: (1) a proposal–critique negotiation protocol in which a Planning Agent generates degree plans evaluated in parallel by domain-specialized agents, enabling detection of cross-domain constraint violations that single-pass outputs miss; (2) a real-time transparency interface streaming agent reasoning and negotiation rounds to users, supported by pilot feedback showing increased trust over standard LLM chatbots; and (3) TartanBench, a difficulty-stratified benchmark of 220 advising queries across five complexity tiers, released open-source without exposing individual student records. A five-configuration ablation study establishes a complexity–necessity curve: single-agent systems perform competitively on simple queries, while multi-agent coordination yields gains of up to +31 points on planning tasks.