Artificial Intelligence for Space Operations: Enabling End-to-End ConOps Design, Autonomous Mission Planning, Anomaly Detection, FDIR, and Resilient Operations

The increasing complexity of contemporary space missions, the rapid expansion of satellite constellations, and the growing reliance on onboard autonomy are transforming the way spacecraft operations are designed and managed. Traditionally, the ConOps is manually derived from mission objectives and operational requirements, while fault management strategies and FSW behavior are developed through separate engineering processes. Maintaining consistency between operational concepts, fault detection and recovery strategies, and onboard software implementations therefore requires extensive manual coordination between mission operations engineers, system engineers, and flight software developers. As spacecraft systems become increasingly complex and operational timelines accelerate, document-centric development approaches struggle to maintain consistency across these engineering artifacts. Changes to spacecraft design often require manual updates across multiple documents and software specifications, increasing the risk of inconsistencies between mission operations procedures, onboard autonomy logic, and fault management architectures. This paper proposes an AI-assisted framework for integrated mission operations architecture development that simultaneously synthesizes operational concepts, FSW logic, and fault management strategies from structured mission knowledge. The framework combines knowledge-graph–based mission representation, retrieval-augmented reasoning using a multi-agent system of LLMs, and MBSE validation to generate operational architectures directly from mission objectives, subsystem capabilities, and system constraints. By structuring mission knowledge as a machine-interpretable graph of mission states, operational actions, subsystem dependencies, and system constraints, the proposed framework transforms spacecraft operations design from a document-centric process into a structured, data-driven architecture. Within this architecture, collaborative AI agents can traverse mission relationships to derive operational timelines, spacecraft autonomy logic, and FDIR strategies that remain consistent with spacecraft system models. A distinguishing characteristic of the proposed framework is the synchronized generation of multiple operational artifacts including ConOps, FSW operational logic, and FDIR strategies, from a unified mission knowledge model. This approach enables consistent operational architectures spanning mission planning, spacecraft autonomy, and fault management while maintaining traceability to mission requirements and subsystem constraints.