In recent years, the interplay between AI agents and blockchain has enabled secure and scalable collaboration among multi-agent systems, promoting unprecedented levels of autonomy and interoperability. AI agents play a vital role in facilitating complex decision making and improving operational efficiency in blockchain systems. This collaborative synergy is particularly evident in how multi-agent systems collectively tackle complex tasks to ensure seamless integration within these frameworks. While significant efforts have been made to integrate AI agents and blockchain, most studies overlook the broader potential of AI agents in addressing challenges such as interoperability, scalability, and privacy issues. In this paper, we bridge these gaps by illustrating the interplay between AI agents and blockchain. Specifically, we explore how AI agents enhance decentralized systems and examine blockchain’s role in enabling secure and scalable collaboration. Furthermore, we categorize practical applications across domains, such as Web3, decentralized finance (DeFi), asset management, and autonomous systems, providing practical insights and real-world use cases. Additionally, we identify key research challenges, including the complexities of multi-agent coordination, interoperability across diverse systems, and privacy maintenance in decentralized frameworks. Finally, we offer future directions in terms of governance, sovereignty, computation, and interpretability to promote a secure and responsible ecosystem.

Data annotation serves as a critical foundation for artificial intelligence (AI) and machine learning (ML). Recently, AI agents powered by large language models (LLMs) have emerged as effective solutions to longstanding challenges in data annotation, such as scalability, consistency, cost, and limitations in domain expertise. These agents facilitate intelligent automation and adaptive decision-making, thereby enhancing the efficiency and reliability of annotation workflows across various fields. Despite the growing interest in this area, a systematic understanding of the role and capabilities of AI agents in annotation is still underexplored. This paper seeks to fill that gap by providing a comprehensive review of how LLM-driven agents support advanced reasoning strategies, adaptive learning, and collaborative annotation efforts. We analyze agent architectures, integration patterns within workflows, and evaluation methods, along with real-world applications in sectors such as healthcare, finance, technology, and media. Furthermore, we evaluate current tools and platforms that support agent-based annotation, addressing key challenges such as quality assurance, bias mitigation, transparency, and scalability. Lastly, we outline future research directions, highlighting the importance of federated learning, cross-modal reasoning, and responsible system design to advance the development of next-generation annotation ecosystems.

In the context of healthcare, the Internet of Things (IoT) relates to a collection of physically linked objects that may share data. This network comprises a variety of medical equipment, wearables, and sensors. The Internet of Medical Things (IoMT) concentrates exclusively on IoT applications in the medical field, with an emphasis on gadgets connected to patient care and health monitoring. Large volumes of health-related data are generated by these networked devices, which introduces the concept of big data. Patient records, measurements from real-time monitoring, and other health-related data are all included in this data. Artificial intelligence's machine learning subfield enhances big data analytics by allowing computers to learn from the data they collect and gradually become more efficient. Machine learning algorithms may be used in healthcare for illness detection, therapy optimization, customized medication, and analytical forecasting. There are several possible advantages to integrating these technologies in healthcare. Proactive intervention, early anomaly identification, and continuous health tracking are made possible by real-time monitoring via IoT/IoMT devices. But there are additional issues with data security, privacy, interoperability, and the moral use of patient data that come with this intersection. To deal with such issues, federated learning (FL) is playing a very crucial role in privacy-preserving machine learning (PPML) in the smart healthcare paradigms. To further improve the security of FL, differential privacy (DP) and homomorphic encryption (HE) are mostly integrated into the PPML approaches. This chapter focuses on security enhancements in the area of healthcare with the utilization of FL, DP, and HE. The study discusses recent approaches, trends, and some future research directions in the PPML for efficient services in healthcare. The present investigation included providing an extensive synopsis of the body of literature related to the field of study. The study has shown how to analyze literature from a variety of angles, which will give researchers evidence to develop original solutions in the field.

With advancements in communication technologies, transitioning from 5G to 6G systems has led to exponential data growth, requiring secure and efficient data transmission solutions. This study integrates data compression techniques Huffman Coding, Lempel Ziv Welch (LZW), and Run-Length Encoding (RLE) with symmetric encryption algorithms, AES (Advanced Encryption Standard) and DES (Data Encryption Standard). The primary goal is to enhance computational performance while ensuring data security. Using a 32 byte dataset and implementing the algorithms in Go language via Visual Studio IDE, results demonstrate the significant reduction in encryption time when combining compression and encryption. Among the AES combinations, AES with Huffman Coding showed the highest efficiency, reducing encryption time by approximately 15 Percent compared to standalone AES. Similarly, DES paired with LZW compression achieved a 20 Percent improvement in computational time over standalone DES. The findings emphasize that selecting the optimal combination depends on data type and user requirements, facilitating secure and efficient communication in high bandwidth, low latency 6G systems. This research underscores the potential of cryptography, combined with compression, to enhance data transmission efficiency without compromising security. The integration approach highlights cryptographic strength in safeguarding big data, addressing challenges in modern technologies like the Internet of Everything (IoE). These results establish a foundation for future secure communication frameworks, promoting reliable and scalable cryptographic solutions tailored for 6G and beyond.