As we see a rapid adoption of data-driven systems and cloud-based infrastructures, organizations are looking to automate their data management processes. Most common ways for implementing data validation, workflow orchestration, and cost optimization are still based on rules which can hardly be used in a changing environment which you have when working with largest amount of data. We describe the design and implementation of intelligent autonomous agents that utilize artificial intelligence and machine learning techniques to improve data validation, increase orchestration efficiency, and minimize operational costs in dynamic environments. This KDD provides insights into novel algorithms that combine multi-agent systems with reinforcement learning and anomaly detection models, facilitating real-time decision-making and adaptive control for autonomous agents. Autonomous agents, for example, help validate the integrity of data by flagging inconsistencies, missing values and anomalies as they develop; orchestration agents manage data pipelines and resource allocation across distributed systems on-the-fly. For example, cost optimization agents use predictive analytics to analyze resource utilization patterns in order to recommend more cost-effective configurations, which will lower computational costs as well as cloud expenses. Related experiments show that the proposed system outperforms conventional methods in terms of data quality, manually interact with as well as overall efficiency. Results demonstrate significant improvements in processing speed, accuracy of anomalies detected, and savings to overall costs. This work is a step toward scalable, intelligent and self-optimizing data ecosystems, making it particularly relevant in cloud computing, enterprise data platforms and AI-powered analysis systems