Agentic-AI-The-Next-Wave-of-Intelligence.pptx

Agentic-AI-The-Next-Wave-of-Intelligence.pptx

• 1.
  Agentic AI: TheNext Wave of Intelligence This presentation explores the emerging field of agentic AI, its key characteristics, and potential to revolutionize how we interact with technology. by Mosiuoa Wesi
• 2.
  What is AgenticAI? Beyond Reactive Agentic AI goes beyond reactive AI, which simply responds to stimuli. It actively pursues goals and objectives, adapting its actions based on feedback and learning from experience. Proactive and Adaptive Instead of passively waiting for instructions, agentic AI systems are proactive and adaptive. They can anticipate needs, generate solutions, and autonomously make decisions within defined parameters.
• 3.
  Key Characteristics of AgenticAI 1 Goal-Oriented Agentic AI systems are designed with specific goals in mind, driving their actions and decisions. 2 Autonomous They can operate independently, making decisions and taking actions without constant human intervention. 3 Self-Learning Agentic AI continuously learns and improves its performance based on data and feedback. 4 Contextual Awareness They can understand and adapt to changing contexts, making decisions based on real-time information.
• 4.
  Benefits of AgenticAI Increased Efficiency Agentic AI can automate tasks, optimize processes, and free up human resources for more strategic work. Enhanced Productivity By making intelligent decisions and taking proactive actions, agentic AI can boost productivity across industries. Improved Decision- Making Leveraging data analysis and predictive modeling, agentic AI can support better informed decision-making. Personalized Experiences Agentic AI can tailor experiences to individual needs and preferences, leading to greater customer satisfaction.
• 5.
  Agentic AI inAction: Use Cases and Examples Manufacturing Automated production lines, predictive maintenance, and quality control. Healthcare Personalized treatment plans, drug discovery, and patient monitoring. Finance Fraud detection, algorithmic trading, and risk management. Education Personalized learning, adaptive tutoring systems, and automated grading.
• 6.
  Agentic AI DesignPatterns 1 Goal Decomposition Breaking down complex goals into smaller, achievable tasks. 2 Reinforcement Learning Training AI agents through rewards and penalties. 3 Hierarchical Planning Organizing tasks and actions into a multi-level hierarchy. 4 Multi-Agent Coordination Enabling multiple AI agents to work together towards a common goal.
• 7.
  Challenges and Considerationsin Implementing Agentic AI Ethical Implications Bias, accountability, and responsible development. Data Privacy and Security Safeguarding sensitive data and preventing misuse. Explainability and Transparency Understanding how AI systems make decisions. Integration and Compatibility Seamlessly integrating AI systems with existing infrastructure.
• 8.
  The Future ofAgentic AI and Emerging Trends 1 Human-AI Collaboration 2 Augmented Intelligence 3 Empowered Decision-Making 4 Adaptive Systems 5 Intelligent Automation