Toward autonomous artificial intelligence agents in sports science: a modular framework for development, validation,...

Despite widespread artificial intelligence (AI) adoption in sports science for predictive analytics, current systems operate as passive tools requiring continuous human monitoring and intervention at every decision point. Autonomous AI agent systems capable of 24/7 monitoring, independent reasoning, and proactive action execution remain academically unexplored in sports science contexts. Unlike passive analytics that await human analysis or conversational interfaces requiring explicit prompting, autonomous agents operate continuously, detecting patterns and implementing interventions without human initiation. Our review distinguished autonomous agents from existing AI applications, proposes modular implementation frameworks, develops theoretical application workflows across eight priority domains, and establishes empirical validation pathways. Current (i.e., April 23, 2026) literature lacks peer-reviewed research on autonomous agent systems in sports science. This review connects computer science with exercise physiology. We integrate modern agent architectures with established sports science concepts. The outcome is a practical, multi-domain implementation roadmap. Our three-phase framework progresses from specialized single-domain agents through coordinated multi-agent systems to fully integrated platforms. Phase 1 develops autonomous agents for training load management, exercise prescription, biomechanical analysis, nutrition optimization, sleep monitoring, injury prevention, mental skills training, and rehabilitation, each operating independently within defined safety boundaries. Phase 2 stablishes coordination protocols enabling information exchange across domains while maintaining modular independence. Phase 3 integrates fully autonomous agent systems across all domains into a unified platform with comprehensive cross-domain reasoning. This framework aimed to advance autonomous agent research from conceptual proposal to a structured implementation and validation pathway in evidence-based athlete management.