Introducing PLUTO (Plain Language Understanding for Trajectory Optimization), our new research exploring how #agentic# #AI# can make sophisticated #aerospace# #engineering# tools more accessible. PLUTO combines an AI coding agent with established numerical optimization methods, using sequential convex programming as its backbone. The agent translates natural-language requirements into executable constraints, formulates the optimization problem, and helps visualize and verify the resulting trajectory. The key idea: let AI handle problem formulation while relying on rigorous numerical methods to solve it. 📄 Paper: 💻 Code: Coming soon. This is our first step toward accelerating aerospace mission design and operations through agentic AI workflows. Stay tuned for more soon! Work led by Eleanor Brosius, in collaboration with Yuji Takubo, @DanieleGammelli, and Simone D'Amico #AgenticAI# #Aerospace# #TrajectoryOptimization# #AutonomousSystems# @StanfordEng @StanfordAILab @StanfordASL