Axis Product Information API (PIA) is an internal REST interface exposing product hierarchy, specifications, compatibility relations, and lifecycle links. This thesis designs, implements, and evaluates a multi-agent LLM system that lets users query this structured product data in natural language. Three experiments contrast (RQ1) orchestrator–worker versus single-agent decomposition, (RQ2) on-demand hierarchy traversal versus LLM-driven traversal, and (RQ3) two-tier reflection versus no reflection. The traversal layer removes traversal-side LLM calls and reaches 100% validity on deep traversals where the historical LLM-recursive baseline reaches 0%, while remaining statistically indistinguishable overall from a tuned one-shot LLM baseline. Reflection-mediated recovery, with predefined clarification supplements, improves validity by 20 percentage points at a +30% token cost. The orchestrator–worker design does not outperform a single agent on pooled validity but uses fewer tokens and provides the substrate for both. The findings support bounded, graph-inspired orchestration with explicit recovery gates for enterprise API agents.