Module 1: Introduction to Agentic AI

• What agentic AI is
• Agents vs chatbots vs LLMs
• Real-world applications and value

Module 2: Understanding Agent Systems

• Input → reasoning → action → output
• Role of instructions and context
• Why outputs vary
• Common failure patterns

Module 3: Core Agent Capabilities

• Planning and task decomposition
• Tool usage and API integration
• Memory and context handling
• Reflection and iterative improvement

Module 4: Agent Architectures and Workflows

• Single-agent vs multi-agent systems
• Workflow design (chains, loops, pipelines)
• Coordination and orchestration

Module 5: Building Agent Applications

• Designing agents from scratch
• Creating workflow-based systems
• Use-case-driven development

Module 6: Retrieval and External Knowledge (RAG)

• Connecting agents to external data
• Retrieval pipelines
• Working with knowledge sources

Module 7: Evaluation and Optimization

• Testing outputs
• Debugging failures
• Improving reliability and consistency

Module 8: Deployment and Practical Usage

• Running agents in real environments
• Basic deployment concepts
• Performance considerations

Module 9: Capstone Project

• Define a real-world problem
• Design a reusable agent system
• Validate and optimize outputs
• Present results