This paper summarizes lessons learned from field-based adaptive intervention design, exploring how multi-armed bandit-based adaptive interventions can be integrated into learning engineering workflows to improve the learning experience and outcomes. It outlines the organizational and technical flows of adaptive intervention design, co-aligned with the learning engineering process model, guiding practitioners through opportunities, constraints, and learning engineering considerations at all design stages. The paper highlights synergy points between adaptive interventions, learning engineering, and analytics, focusing on ways to support research and continuous improvement collaborations, increase rigor and efficiency, and knowledge mobilization. The proposed workflows aim to accelerate evidence-based improvement in digital education, translating improvement ideas into real-world classroom adaptive interventions. This work sets a foundation for supporting the design of data-driven adaptive interventions that enhance the learning experience and outcomes at scale.