The strategic placement of sensors in flight test vehicles is critical for acquiring relevant data while minimizing weight, cost, and system complexity. Traditional sensor placement optimization relies on iterative human-driven analysis, computational analysis, and heuristic approaches that are time-intensive and may not explore the full solution space. We present a novel framework employing agentic AI workflows to autonomously optimize sensor configurations for flight test instrumentation. Our approach addresses the challenge of optimally positioning any type of thermal response sensors, accelerometers, and strain gauges throughout the vehicle structure. The problem is formulated as a constrained optimization where sensor placement must maximize measurement coverage and data quality while adhering to strict weight budgets and spatial limitations imposed by the vehicle geometry and existing systems. An AI agentic wrapper invokes a Python package called Pysensors [1] to perform the optimization. For example, the optimizer performs thermal analysis to identify critical heat flux zones and temperature, or structural analysis to determine optimal accelerometer locations for capturing modal responses and flight loads or strain analysis to select strategic positions for detecting structural stress concentrations. The agent ensures that all placements comply with weight allocations, physical accessibility, and electromagnetic interference requirements. The agent can handle more than one type of sensor simultaneously. The agentic workflow autonomously explores the solution space, evaluating candidate sensor configurations against mission-specific measurement objectives while respecting design constraints. This intelligent framework reduces the burden on human engineers, systematically considers interdependencies between sensor types, and identifies placement strategies that balance competing requirements. The methodology provides a scalable, adaptable approach to instrumentation design that can accommodate diverse vehicle configurations and test objectives, ultimately accelerating flight test program development while ensuring comprehensive data acquisition within practical engineering constraints.