Certified Training

Agentic AI Build Your First AI Agent

  • April 9-10, 2026
  • 10:00 AM - 3:00 PM
  • 14 Modules
Trainer
Jayantilal Bhanushali

Certified Generative AI and Data Science Trainer

+
DAY 1
Foundations, Paradigm, Capabilities, Automation, Strategy & Frameworks

April 9, 10am – 3pm

DAY 2
Deployment, Governance, Evaluation, Security, Ethics, Stack & Use Cases

April 10, 10am – 3pm

Module 1

AI Agents - Introduction

  • 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

Module 2

Agentic AI Paradigm

  • 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

Module 3

Agent Capabilities

  • 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 (E
  • xamples: 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)

Module 4

Automation and Workflow Optimization

  • 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)

Module 5

AI Strategy for Businesses

  • Identifying AI opportunities in your business
  • Frameworks for AI adoption in organizations
  • Aligning AI implementation with business goals
  • Overcoming common challenges in AI adoption

Module 6

Frameworks

  • Introduction
  • HuggingFace
  • Ollama+DeepSeekV3
  • AutoGen
  • LangChain
  • CrewAI
  • LangGraph
  • Pydantic AI
  • AutoGPT
  • Comparison of frameworks

Module 7

Post Deployment

  • Monitoring, Evaluation, and Debugging
  • AgentBench
  • AgentOps
  • LangSmith
  • Langfuse
  • Logfire in Pydantic AI
  • Metrics
  • Monitoring Demo

Module 8

AI Governance & Risk Management

  • How to assess AI risks in business operations
  • Governance frameworks for responsible AI use
  • Strategies for mitigating AI risks

Module 9

Evaluating AI Agent Performance (Business Metrics)

  • 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

Module 10

AI Agents Security

  • Introduction to AI Agents Security
  • Vulnerabilities & Mitigation
  • Guardrails
  • Tools

Module 11

Future Trends in Agentic AI

  • 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

Module 12

Ethical Design of AI Agents

  • Ethical Design of AI Agents
  • Addressing Challenges

Module 13

Technology Stack

  • Technology Stack for Building Agentic AI Systems & Architecture

Module 14

Use Cases

  • Siemens AG
  • Mayo Clinic
  • JPMorgan Chase
  • Amazon
  • BP (British Petroleum)
  • Pearson
  • Netflix

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