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Dev
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Introduction: The Evolution of AI to Agentic AI
- Agentic AI signifies a notable advancement in AI, offering autonomous decision-making capabilities and adaptive behavior.
- It operates independently, learns from its environment, and continuously optimizes actions to achieve set goals.
- Agentic AI evolves from traditional rule-based AI to autonomous task performance systems.
- Key features include goal-oriented decision-making, real-time adaptation, learning capabilities, and autonomy.
- The architecture of Agentic AI comprises sensor/perception, cognitive processing, action/execution, and feedback/learning layers.
- Applications of Agentic AI range from autonomous vehicles and business automation to healthcare and smart cities.
- Challenges include accountability, bias, security risks, and societal impact due to potential job displacement.
- Despite challenges, Agentic AI has the potential to revolutionize industries by enhancing efficiency and providing personalized solutions.
- Ethical considerations include accountability, fairness, security risks, and societal impact.
- The future of Agentic AI depends on addressing ethical and regulatory concerns while ensuring transparent and secure AI systems.
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