## 1 Introduction
### Motivation and Contributions  
The integration of AI agents into data annotation workflows addresses critical gaps in current methodologies while introducing novel capabilities that extend beyond traditional automation approaches. While the existing literature has explored individual aspects of AI-assisted annotation, such as active learning, weak supervision, and LLM-based labeling, there remains a significant research gap in understanding how autonomous agent architectures can orchestrate comprehensive annotation pipelines [18; 19]. Current annotation systems lack the sophisticated reasoning, adaptive decision-making, and collaborative capabilities that AI agents can provide, particularly in complex scenarios requiring multi-step workflows, quality assurance mechanisms, and dynamic guideline interpretation [20]. Moreover, the rapid evolution of LLMs has outpaced the development of systematic frameworks for leveraging their capabilities in annotation contexts, creating an urgent need for comprehensive analysis of agent-driven annotation paradigms.  
Our survey addresses key limitations in the existing literature by offering the first in-depth analysis of the transformative role of AI agents in data annotation, bridging theoretical foundations with practical insights. We introduce a novel taxonomic framework for categorizing agents based on their capabilities and analyze how LLM-enhanced agents employ advanced reasoning strategies, such as Chain-of-Thought, Tree-of-Thought, and ReAct, to improve annotation outcomes across modalities. We further examine current trends in agent-driven system design and evaluation, offering insights into their operational efficiency, quality control, and real-world adaptability. In addition, we explore the practical landscape of agent-based tools and highlight ongoing research frontiers that will shape future development in this field. The contributions of our survey are summarized as follows:  
* Comprehensive Taxonomy and Technical Analysis: We establish the first comprehensive framework that systematically classifies AI agents in data annotation. This contribution integrates a novel taxonomy based on agent capabilities with a deep analysis of architectural patterns ranging from single-agent systems to multi-agent collaborations to provide unified design principles and implementation guidelines for researchers and practitioners.
* Architectural Design and Evaluation Framework: We systematically examine various architectural approaches for agent-driven annotation systems, including single-agent pipelines, multi-agent collaborations, and human-in-the-loop (HITL) integration, providing practical design guidelines and trade-off considerations. Complementing this, we introduce a holistic evaluation framework with novel metrics and standardized benchmarks to rigorously assess agent performance, economic impact, and quality assurance.
* Real-world Applications and Tools: We present a thorough analysis of the practical ecosystem by examining transformative, real-world applications across diverse industries (e.g., healthcare, finance, and technology). This is paired with a critical assessment of the current landscape of tools, platforms, and frameworks, offering actionable guidance for technology selection and strategic implementation.
* Research Challenges and Future Directions: We systematically identify critical open challenges, including quality assurance, bias mitigation, transparency, privacy, and scalability in AI-agent-driven annotation. Based on this analysis, we outline a forward looking research roadmap to guide the future development of robust, reliable, and responsible AI annotation agents.