@Article{Dergaa2026,
journal="Biology of Sport",
issn="0860-021X",
year="2026",
title="Toward autonomous artificial intelligence agents in sports science: 
a modular framework for development, validation, and 
implementation",
abstract="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.    Video abstract: https://www.youtube.com/watch?v=MPL-NP8kzpo\&feature=youtu.be",
author="Dergaa, Ismail
and Barbaria, Sabri
and Dhahbi, Wissem
and Zmijewski, Piotr
and Chamari, Karim
and Ben Saad, Helmi",
pages="1353--1426",
doi="10.5114/biolsport.2026.161708",
url="http://dx.doi.org/10.5114/biolsport.2026.161708"
}