Agentic AI Mastery Program | Build AI Agents & Automation Skills

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Agentic AI Mastery Program

Sessions Venue
Online & On Campus Apply Now

What You Will Learn

Fundamentals of AI & Agentic AI

Prompt Engineering & AI Communication

Building AI Agents with Modern Frameworks

Automation of Business Workflows

API Integrations & Tool Connectivity

Real-World AI Projects & Use Cases

Deployment & Scaling of AI Solutions

Course Modules

This program introduces you to AI and agentic systems, covering prompt engineering, AI agent development, integrations, automation, and deployment. You will gain practical knowledge through real-world use cases and hands-on projects, enabling you to build, deploy, and scale AI-powered solutions effectively.

At the end of this program, you will be able to:

  • Understand AI & Agentic Systems: Learn the fundamentals of AI, machine learning, and agent-based systems along with real-world applications.
  • Master Prompt Engineering: Write effective prompts, manage context, and apply role-based prompting techniques.
  • Build AI Agents: Create task-based and multi-step reasoning agents for solving complex problems.
  • Integrate AI with Tools: Connect APIs, external tools, and automate workflows efficiently.
  • Deploy & Scale Solutions: Deploy AI systems, optimize performance, and scale solutions for real-world use.

Module Breakdown

Now…here’s something SUPER EXCITING that we have to share with you…

This is a complete overview of the actions you will take while building your AI skills.

  • Goal:
    • Understand AI systems + run your first local AI engine
  • Topics:
    • What is AI, ML, Deep Learning, Generative AI
    • What is Agentic AI (modern definition)
    • AI vs AI Agents vs AI Automation
    • Real-world AI systems architecture
    • Introduction to local AI vs cloud AI
  • Hands-on (Ollama Focus):
    • Install and configure Ollama
    • Run LLMs locally (Llama3, Mistral, DeepSeek)
    • Compare model behavior
    • Understand tokens, context window, latency
  • Outcome:
    • Students can run AI models locally
    • Understand AI system architecture
    • Understand where agents fit in real world
  • Goal:
    • Control AI behavior effectively
  • Topics:
    • Prompt engineering fundamentals
    • Role-based prompting
    • System vs user prompts
    • Context engineering (critical skill)
    • Structured outputs (JSON responses)
    • Prompt chaining concepts
  • Hands-on (LangChain intro)
    • Build prompt templates using LangChain
    • Create structured AI responders
    • Build reusable prompt pipelines
  • Outcome:
    • Students can design reliable AI outputs
    • Understand context control in AI systems
  • Goal:
    • Move from “chatbots” → “autonomous systems”
  • Topics:
    • What is an AI agent (real definition)
    • Agent lifecycle (observe → think → act)
    • Tool calling concept
    • Single-step vs multi-step reasoning
    • Memory in AI systems
  • Hands-on
    • Build task-based AI agent (LangChain)
    • Build multi-step reasoning agent (LangGraph)
    • Create planner-executor workflow
  • Outcome:
    • Students build first AI agent
    • Understand decision-making workflows
  • Goal:
    • Connect AI to real systems
  • Topics:
    • REST APIs for AI systems
    • FastAPI introduction
    • Tool calling with external APIs
    • Webhooks concept
    • Introduction to workflow automation
  • Hands-on
    • Build AI API with FastAPI
    • Connect AI to external services
    • First n8n workflow (basic automation trigger)
  • Outcome:
    • Students can connect AI to external systems
    • Understand backend AI architecture
  • Goal:
    • Turn AI into business workflows
  • Topics:
    • Workflow automation fundamentals
    • Event-driven systems
    • AI + business process automation
    • Triggers, actions, pipelines
  • Hands-on (n8n heavy)
    • AI content generation pipeline
    • Customer support automation system
    • Email automation system
    • CRM-style automation workflow
  • Outcome:
    • Students can automate real business workflows
    • Understand AI as operational engine
  • Goal:
    • Build production-style AI assistant
  • Topics:
    • Multi-agent orchestration (LangGraph)
    • Memory systems
    • Tool orchestration
    • RAG integration basics
    • AI system design patterns
  • Hands-on
    • Build AI assistant using Ollama + LangGraph
    • Add memory + tools
    • Connect to vector database (RAG light version)
    • Deploy via FastAPI
  • Outcome:
    • Students build full AI assistant system
    • Understand production AI architecture
  • Goal:
    • Make AI systems production-ready
  • Topics:
    • Latency optimization
    • Token optimization
    • Caching strategies
    • Scaling AI services
    • Docker basics
    • Deployment architecture
  • Hands-on
    • Dockerize AI application
    • Deploy Ollama locally/server
    • Host FastAPI AI service
    • Connect n8n + API system
  • Outcome:
    • Students deploy real AI systems
    • Understand production readiness

MOMENTS TO BE REMEMBERED