What is Agentic AI?
How it differs from traditional AI
Key capabilities: autonomy, decision-making, adaptability
Real-world examples (self-driving cars, intelligent assistants, automated research agents)
2. Introduction to
Agentic AI
What is Agentic AI?
How it differs from traditional AI
Key capabilities: autonomy, decision-making,
adaptability
Real-world examples (self-driving cars,
intelligent assistants, automated research
agents)
3. Why Agentic AI Matters in
2025 and Beyond
Growing need for autonomous systems
Efficiency gains in business processes
Real-time decision-making potential
Industry adoption trends
4. Learn Python, Data Science, and Machine
Learning basics
Understand neural networks and deep learning
Explore reinforcement learning (RL) basics
Recommended tools: TensorFlow, PyTorch,
Scikit-learn
Build
Strong
AI
Fundamentals
5. Master Reinforcement Learning
(RL)
Why RL is core to Agentic AI
Key concepts: agents, environments, rewards, policies
Study Q-Learning, Deep Q-Networks (DQN), PPO
Hands-on practice with OpenAI Gym environments
6. Integrating RL with planning algorithms
Multi-agent systems and coordination
Adaptive and self-improving agents
Example: AI-driven financial trading bots
Learn Autonomous Decision-
Making Systems
7. Work with AI-Orchestration
Frameworks
LangChain, Auto-GPT, BabyAGI
Connecting multiple AI models for
autonomous workflows
Using APIs and agents together
Case study: Automated research
assistant
8. Choose a domain (healthcare, finance, robotics,
gaming)
Prototype →test →iterate
Deploy on cloud (AWS, Azure, GCP)
Monitor and refine with feedback loops
Build Real-World
Agentic AI
Projects
9. Ethics, Safety &
Regulation
AI alignment and safe decision-making
Bias detection and transparency
Compliance with AI regulations (EU AI
Act, global policies)
Building trustworthy AI agents
10. Your Learning Path & Next
Steps
Suggested timeline (Beginner →Intermediate →Advanced →Expert)
Continuous learning via research papers & AI communities
Networking with AI professionals
Final motivation: “Build agents that think, learn, and act for good”
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