Course curriculum

  • 1
    Module 1 : AI Agents - Introduction
  • 2
    Module 2 : Agentic AI Paradigm
  • 3
    Module 3: Agent Capabilities
  • 4
    Module 4 : Automation and Workflow Optimization
  • 5
    Module 5: AI Strategy for Businesses
  • 6
    Module 6 : Frameworks
  • 7
    Module 7: Post Deployment
  • 8
    Module 8: AI Governance & Risk Management
  • 9
    Module 9: Evaluating AI Agent Performance (Business Metrics)
  • 10
    Module 10: AI Agents Security
  • 11
    Module 11: Future Trends in Agentic AI
  • 12
    Module 12: Ethical Design of AI Agents
  • 13
    Module 13: Technology Stack
  • 14
    Module 14: Use Cases
  • Definition of AI Agents Importance of AI Agents in Modern AI Real-World Applications How AI Agents Work Types of Environments for AI Agents Evolution of AI Agents
  • Introduction to the Agentic AI Paradigm Key Characteristics of Agentic AI Core Components of Agentic AI Systems Comparison with Other AI Paradigms Architectures and Frameworks in Agentic AI Challenges in Developing Agentic AI Systems Economic Impact
  • Perception and Recognition Capability Decision-Making and Problem Solving Capability Learning and Adaptation Capability Action and Interaction Capability Multimodal Functionality in AI Agents Tools and Frameworks for AI Agents Data Processing Capabilities Retrieval-Augmented Generation (RAGs) Database Integration for AI Agents (Examples: Supabase, Firebase, MongoDB, and PostgreSQL) Advanced Agent Capabilities (Adaptive and Self-Learning Agents, Collaboration in Multi-Agent Systems (MAS), Long-Term Memory and Context Awareness)
  • Introduction to AI Automation and Workflow Optimization Core Components of AI-Driven Workflow Optimization Types of Workflows Optimized by AI Agents and use case applications Automation Techniques Leveraged by AI Agents Tools and Platforms for AI Workflow Automation (e.g., Zapier, UiPath, Blue Prism, n8n, Langgraph, pydantic) Advanced Capabilities in AI Automation (Dynamic Task Scheduling and Context-Aware Automation, Proactive Agents, Adaptive Workflows)
  • Frameworks for AI adoption in organizations Aligning AI implementation with business goals Overcoming common challenges in AI adoption
  • Introduction HuggingFace Ollama+DeepSeekV3 AutoGen LangChain CrewAI LangGraph Pydantic AI AutoGPT Comparison of frameworks
  • Monitoring, Evaluation, and Debugging AgentBench AgentOps LangSmith Langfuse Logfire in Pydantic AI Metrics Monitoring Demo
  • How to assess AI risks in business operations Governance frameworks for responsible AI use Strategies for mitigating AI risks
  • How to measure the success of AI Agents in business settings Key performance indicators (KPIs) for AI-driven automation Understanding AI reliability, efficiency, and user experience Case studies on AI performance evaluation
  • Introduction to AI Agents Security Vulnerabilities & Mitigation Guardrails Tools
  • Emerging technologies in AI Agents The role of AI Agents in the future of work Predictions for AI in the next 5–10 years How businesses can stay ahead in the AI revolution
  • Ethical Design of AI Agents Addressing Challenges
  • Technology Stack for Building Agentic AI Systems & Architecture
  • Siemens AG Mayo Clinic JPMorgan Chase Amazon BP (British Petroleum) Pearson Netflix