Agentic AI Mastery Program | Build AI Agents & Automations | Enablers

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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

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Agentic AI Mastery

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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