External Quantum Reservoir Guidance for Complex AI-Assistant Tasks

External Quantum Reservoir Assist is designed to help an AI assistant identify the core of a difficult request and organize a useful response by adding dynamically generated guidance through a REST or MCP integration. This study evaluates final answers from an archived experiment using one response pair from each of 50 synthetic scenario families. Thirty-nine pairs formed the primary complex decision-support group, while eleven provided routine, constraint-challenge, and technical-explanation controls. Within the primary group, reservoir-assisted answers were preferred in 17 cases, baseline answers in 8, with 13 ties and 1 unclear case. The tie-adjusted preference rate was 61.8% (95% family-bootstrap interval: 48.7–73.7%). The clearest descriptive gains were observed in actionability and validation guidance, while major-error flags occurred equally often in both conditions. Assistance increased total token usage by 32.9% and median paired estimated end-to-end time by 7.51 seconds. The results provide directional evidence that external reservoir guidance can improve practical decision support for selected complex AI-assistant tasks, while also demonstrating substantial uncertainty and operational overhead. The study does not establish general response superiority, reduced factual-error rates, or a quantum-specific advantage. The accompanying minimized dataset and executable analysis reproduce the reported statistics and figures without additional model calls.