Agentic AI frameworks are a structured way of building AI systems that can observe, plan, and take action based on goals and available information. These systems are designed to go beyond basic automation by enabling agents to understand their environment, remember past interactions, and take steps that lead toward a specific outcome. An agentic framework typically includes components such as input processing, memory handling, task planning, and execution. Together, these parts allow the AI agent to work through tasks in steps, adjusting its behavior as needed. This is different from traditional models that follow fixed instructions. Instead, agentic systems can make decisions as they move through a process. Dataplatr uses this structure to bring more flexibility and control into how AI interacts with complex data systems. By organizing tasks through agentic principles, Dataplatr helps teams handle tasks like analyzing datasets, managing data flows, or detecting issues—all with less manual input. Another example is the databricks agent framework, which applies similar ideas to data engineering and analytics environments. It allows agents to take actions like querying data, modifying pipelines, or generating reports. In this way, an Ai agent framework supports systems that can adapt, act, and improve over time—clearly showing What is agentic framework in real-world use. These capabilities make Agentic AI frameworks a strong foundation for smarter applications.