Description: Artificial Intelligence is rapidly moving from systems that only generate responses to systems that can work toward broader goals. Agentic AI is an emerging approach in which an AI system can interpret an objective, plan a sequence of tasks, use external tools, observe the results of its actions, and adapt its approach when necessary. Instead of treating every request as a separate question, an agent can maintain a task context and coordinate several operations to reach a desired outcome. Agentic AI combines foundation models with tools, memory, reasoning, planning, feedback, and safety controls. It can therefore connect language-based intelligence with real software actions such as searching information, reading files, querying databases, running code, communicating with applications, and preparing results. Recent work from major AI organizations is focusing on long-horizon agents, controlled execution environments, evaluation methods, and communication between independen...
ABSTRACT : The agricultural sector is moving through a phase of rapid technological change driven by Artificial Intelligence (AI) and robotics. Traditional farming methods are struggling to cope with modern issues such as shortage of skilled labor, irregular climate patterns, excessive use of water and fertilizers and the increasing pressure to produce more food for a growing population. To overcome these challenges, farmers are turning toward intelligent technologies that make agriculture more precise, automated and efficient. Artificial Intelligence enables farms to operate based on data rather than guesswork. Advanced algorithms analyze information related to soil properties, moisture levels, crop growth stages, weather conditions and pest behavior. Using tools like machine learning and computer vision, AI systems help farmers plan irrigation, monitor crop health, control pests and predict yields with greater accuracy. At the same time, robotic machines perform physically dema...