Course Overview
The Agentic AI Course is designed to provide an in-depth understanding of autonomous AI agents, their architecture, and real-world applications. Participants will learn how to build AI agents that can autonomously make decisions, execute tasks, and adapt to dynamic environments. The course covers multi-agent systems, reinforcement learning, cognitive AI, LLM-powered agents, and real-world deployment strategies.
Module 1: Introduction to Agentic AI
- What is Agentic AI? Understanding AI agents vs. traditional AI models
- Applications of autonomous AI agents in different industries
- Agentic AI vs. Generative AI: Key differences and use cases
- Ethical and safety considerations for autonomous AI
Module 2: Fundamentals of AI Agents
- Types of AI Agents : Reactive Agents, Planning Agents, Learning Agents
- Agent Architectures : Perception, Action, Decision-Making
- State Representation & Memory : Short-term vs. Long-term memory for agents
- Goal-Driven & Task-Oriented AI Agents
Module 3: Building Autonomous AI Agents
- Creating AI Agents using LLMs (GPT, Claude, Gemini, etc.)
- Fine-tuning AI Agents for specific tasks
- Prompt Engineering & Chain-of-Thought Reasoning for AI Agents
- Hands-on: Developing an AI assistant that automates tasks
Module 4: Multi-Agent Systems (MAS)
- Understanding Multi-Agent Environments (Cooperative vs. Competitive Agents)
- Communication between AI Agents : LLM-based message passing
- Multi-Agent Reinforcement Learning (MARL)
- Hands-on: Creating a Multi-Agent Collaboration System
Module 5: Reinforcement Learning for AI Agents
- Deep Reinforcement Learning (DRL) for autonomous decision-making
- Reward Functions & Policy Learning
- Q-Learning, PPO, DDPG & Other RL Techniques
- Hands-on: Training an AI Agent to optimize decision-making
Module 6: Memory, Retrieval & Planning in AI Agents
- Adding Memory to AI Agents : Vector Databases, RAG (Retrieval-Augmented Generation)
- Hierarchical Planning & Task Execution
- Tools for autonomous task prioritization
- Hands-on: Building an AI Agent with a persistent memory system
Module 7: Tool-Using AI Agents & API Integration
- How AI Agents use external APIs, databases, and tools
- LangChain & Auto-GPT frameworks
- Automating business workflows with AI agents
- Hands-on: Creating an AI agent that integrates with third-party APIs
Module 8: Self-Learning & Self-Improving AI Agents
- How AI Agents adapt & improve over time
- Meta-Learning & Self-Evolution Strategies
- Automated Model Fine-Tuning & Optimization
- Hands-on : Developing an AI agent that learns from user feedback
Module 9: AI Agents in Real-World Applications
- Autonomous AI Agents in Business, Healthcare, and Finance
- AI Agents for Personal Productivity (Automating Emails, Scheduling, etc.)
- Building Autonomous Research & Trading Agents
- Hands-on : Deploying an AI agent for real-world automation
Module 10: Deployment & Scaling Agentic AI Systems
- Deploying AI Agents in Cloud & Edge Environments
- Scaling AI Agents for high-performance applications
- Monitoring & Optimizing AI Agent Performance
- Final Project: Building and Deploying a Fully Functional AI Agent
Who Should Enroll?
- AI & ML Engineers
- Data Scientists & AI Enthusiasts
- Developers & Software Engineers
- Entrepreneurs & Product Managers
- Business Leaders Exploring AI Automation
Course Benefits
- Hands-on AI Agent Development
- Industry-Level AI Tools & Techniques
- Real-World AI Agent Projects
- AI Certification Upon Completion
- Career Support & Job Assistance
🔗 Enroll Now & Build the Future of AI!
- Autonomous AI Agents Masterclass
- Certified Agentic AI Specialist (CAAS) Program
- Expert Certification in Agentic AI and Autonomous Systems
- Agentic AI Architect Certification Program
- AI Agents & Automation: Advanced Certification Course
- Professional Certification in Agentic AI & Multi-Agent Systems
- Intelligent AI Agents & Automation Mastery Program
- Certified AI Agent Developer (CAAD) Course
- 9. Agentic AI & Reinforcement Learning Specialist Program
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