Static infrastructure planning models fail in a predictable way: they are calibrated on the past and are therefore least accurate precisely when conditions change most rapidly. The problem is not merely one of forecast precision but of architectural rigidity---these systems have no mechanism for updating their assumptions in response to the very outcomes they predict. This paper introduces the Intent-Based Planning Engine (IBPE), a closed-loop, AI-driven framework for adaptive infrastructure demand forecasting that draws its conceptual architecture from two converging technical traditions: intent-based networking and chaos engineering. IBPE integrates multivariate regression, ARIMA time-series modeling, unsupervised behavioral segmentation, structured scenario perturbation, and gradient-based feedback adaptation within a single modular system. The framework's most architecturally distinctive element is an intent modeling layer that disaggregates aggregate demand into behaviorally coherent population segments, each characterized by its own elasticity profile and sensitivity to macroeconomic perturbation. The feedback adaptation mechanism is formally derived from the chaos-level engine paradigm developed in U.S. Patent No. 12,242,370 B2 (Cisco Technology, Inc., 2025), in which controlled perturbation, impact measurement, and parameter correction form an iterative closed loop progressively narrowing the gap between intended and observed system behavior. Experimental evaluation across a 150-unit residential infrastructure simulation demonstrates a 14.2% reduction in mean absolute error over single-method baselines, a 23% improvement in supply-demand alignment through intent-based allocation, a 62.7% cumulative reduction in prediction error over ten feedback cycles, and scenario-driven risk mitigation that reduces supply overcommitment exposure by 31% under adverse macroeconomic conditions. These results establish IBPE as a technically rigorous, domain-portable framework for adaptive planning under uncertainty.