NASA is exploring a bold new frontier in engineering with its Text-to-Spaceship project, which aims to design spacecraft using teams of AI agents, triggered by simple text or voice prompts—similar to the fictional "Jarvis" AI from Iron Man. Led by Ryan McClelland at NASA’s Goddard Space Flight Center, the initiative envisions engineers collaborating in virtual or mixed reality environments with AI assistants to rapidly conceptualize and iterate on spacecraft designs.
The project is powered by Large Language Models (LLMs), which can interpret and act on natural language prompts. Partner companies like Celedon and Synera are central to this effort. Synera contributes by automating data flow across various engineering tools using low-code connectors, which allows AI agents to run simulations and make design adjustments autonomously. Both companies are developing multi-agent systems where AI agents work together like human engineering teams—complete with memory, feedback loops, and hierarchical roles.
This AI-driven design approach could drastically speed up the early concept phase of engineering projects—by up to 100x—reducing manual, repetitive tasks such as data transfer between software tools. However, the rise in productivity may impact engineering employment, especially in entry-level roles. The software industry, for example, has already seen job reductions due to similar AI-driven efficiencies.
Despite these concerns, proponents like McClelland and Synera’s CEO Moritz Maier argue that the technology creates new opportunities. Engineers will increasingly shift from manual work to higher-level tasks like workflow development, agent training, and innovation at the edge of technology. The core of engineering remains intact: defining problems, validating solutions, and asking the fundamental question—is this mission worth doing?
NASA plans to test a suborbital demonstration of this AI-powered design system later in 2025. As AI continues to evolve, it promises to redefine how engineers create, collaborate, and push the boundaries of possibility.
