Building Orbital Brain: A Human-in-the-Loop LLM Architecture for Space Missions
We have been exploring what a practical LLM architecture for space mission operations might look like.
Instead of asking whether AI can control spacecraft, we started with a different question: How can LLMs help mission operators make better decisions while keeping humans firmly in control?
That led to Orbital Brain, a proof-of-concept built around a seven-phase pipeline covering data ingestion, state reasoning, multi-agent analysis, planning, predictive foresight, and explainability.
One deliberate design decision was to keep the LLM out of the command loop. Every output is advisory, traceable, and reviewable, reflecting how safety-critical systems are actually operated.
We would value perspectives from builders working on AI agents, multi-agent systems, or explainable AI. Does this architecture align with how LLMs should be used in high-stakes environments?
Read Full Insight here - https://capestart.com/resources/blog/llm-architecture-for-space-mission/
Keeping the LLM out of the command loop is necessary, but “human in the loop” is not a safety property by itself. Each advisory output needs evidence, confidence, an abstain path, a named approver, and a safe default when nobody responds. The dangerous failure is plausible advice being rubber-stamped when the seven-phase pipeline creates more alerts than operators can inspect.