This thesis comprehensively explores the integration between morphology, movement and sensorimotor system, contributing to our understanding of emergent insect flight agility. As part of the thesis, the discovery of the evolution of two distinct strategies in moth flight informs that neural processing is not isolated. It co-evolves and deeply integrates with the mechanical elements of flight, such as wing morphology and kinematics, and the resultant aerodynamics, thus implementing specialized neural control strategies. Using a quasi-steady aerodynamic model, it is shown that on the one hand, the peculiar biomechanical features of silkmoths introduce body pitch oscillations mid-air, which the neural control successfully compensates for. On the other hand, the flight agility of hawkmoths is primarily facilitated by their high wingbeat frequencies and high-aspect-ratio wings. This requires the nervous system to fire motor commands at a smaller time scale with higher temporal precision. In this thesis, this is captured by a recurrent neural network-based model of the hawkmoth visuomotor system. This model, once interfaced with a model of flight mechanics, will help delve deeper into the integration questions, such as, how sensory cues, that elicit rapid flight maneuvers, modulate temporally precise and coordinated motor commands. Besides this bottom-up approach, the thesis also presents a top-down approach to exploring the integration questions through the decomposition of hawkmoth flower-tracking behavior into sensorimotor system and flight mechanics. While being tightly integrated with a closed loop in flower-tracking hawkmoths, the two subsystems operate in a narrow dynamic regime that elicits emergent linear behavior at the subsystem as well as at the organismal level. Thus, these integrative approaches not only offer insights into the complexity of insect flight but also open new avenues for bio-inspired engineering. By discovering the principles underlying the emergent nature of insect locomotion agility, engineers can draw inspiration for designing more efficient, agile, and adaptable robotic systems.