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Applied Agentic AI Course

Applied Agentic AI Certification is a comprehensive, project-based training program designed to help learners build intelligent AI systems that can reason, plan, use tools, and perform tasks autonomously. This course takes participants beyond traditional AI concepts and prompt en...

  • Live + Recorded
  • 4 Portfolio Projects
  • Rated 4.9 / 5
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Applied Agentic AI Course

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₹45,999 ₹91,998
  • Duration Flexible
  • Format Live + Recorded
  • 4 Portfolio Projects
  • Rated 4.9 / 5
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Overview

Applied Agentic AI Certification is a comprehensive, project-based training program designed to help learners build intelligent AI systems that can reason, plan, use tools, and perform tasks autonomously. This course takes participants beyond traditional AI concepts and prompt engineering, providing hands-on experience in developing real-world AI applications powered by Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, and Multi-Agent Systems.

Through live instructor-led sessions, practical projects, and industry-standard tools, learners will gain the skills required to design, develop, optimize, and deploy production-ready AI solutions. The curriculum covers everything from LLM fundamentals and prompt engineering to advanced RAG pipelines, AI agents, multi-agent architectures, MCP (Model Context Protocol), voice-enabled assistants, and multimodal AI systems.

By the end of the program, participants will have built multiple real-world AI applications, mastered modern AI development frameworks, and gained the expertise needed to create scalable, enterprise-grade AI systems. This certification is ideal for professionals seeking to advance their careers in AI Engineering, Generative AI, Agentic AI, and intelligent automation.

Prerequisites

To get the most out of the course, you should:

  • Be comfortable writing basic Python code
  • Understand simple concepts like loops and functions
  • Have some idea of how APIs connect applications (nice to have, not required)

Key Features

  • 36 Contact Hours with Live, Instructor-Led Sessions

    designed to help you understand and build real-world AI systems step by step.

  • Hands-On Projects & Real AI Systems

    to build practical solutions such as a Mini-GPT model, a RAG pipeline, and both single-agent and multi-agent AI architectures.

  • 10+ Industry Tools Covered

    including hands-on use of production-ready tools such as Claude.ai, LangChain, CrewAI, LangGraph, Chroma, FastAPI, Ollama, and more.

  • Advanced RAG & Retrieval Engineering

    to learn how to connect AI models with real-world data using embeddings, vector databases, chunking strategies, and hybrid search.

  • Agentic AI & Multi-Agent System Design

    focused on building AI agents that reason, use tools effectively, collaborate, and handle complex problems.

  • Production Optimization & Fine-Tuning

    to enhance AI systems for better performance, cost efficiency, and reliability while deploying fine-tuned models in real-world applications.

Curriculum

1. Week 1 - LLM Foundations & System Thinking

Understand how LLMs work - Transformer intuition (simplified), Attention mechanism concept, Tokenization & embeddings, Context window limitations, Hallucination causes, RAG failure modes (intro), Prompt vs Retrieval vs Fine-Tuning - overview comparison enough to engineer systems around them.

Topics

  • Traditional AI vs Generative AI -paradigm shift explained
  • Transformer architecture: attention mechanism, layers, context
  • Tokens, embeddings, and context windows - engineering implications
  • How LLMs generate text: next-token prediction, temperature, sampling strategies
  • LLM limitations: hallucinations, knowledge cutoff, context degradation
  • Introduction to information retrieval: why LLMs alone aren't enough
  • Overview: OpenAI vs Google Gemini vs Anthropic Claude - capability map
  • Safety, responsible AI, and deployment considerations

Hand-ons

  • OpenAI Python SDK
  • Google Gemini SDK
  • Anthropic Claude SDK
  • tiktoken
  • Visual Studio Code/ Google Colab

Project

Multi-SDK LLM Playground

Build a unified interface that queries OpenAI, Gemini, and Claude simultaneously and compares responses side by side.

2. Week 2 - Prompt Engineering & Structured Output

Master advanced prompting techniques and build systems that produce reliable, structured, evaluable outputs.

Topics

  • Zero-shot, few-shot, and chain-of-thought (CoT) prompting - when to use each
  • Tree-of-Thought (ToT) and self-consistency prompting
  • Role prompting and persona design for consistent behavior
  • Designing output constraints: format, length, tone, schema
  • Structured responses: JSON schema enforcement, typed outputs
  • Function calling and tool use: mechanics and design patterns
  • Prompt injection: attack vectors and defense strategies
  • Evaluation-driven iteration: test → measure → improve loops
  • Introduction to prompt versioning and management

Hand-ons

  • OpenAI JSON mode
  • Open Ai Playground/Clause Console/Google Ai studio
  • LangChain PromptTemplate
  • OpenAI Evals (intro)/Claude Console Prompt generator
  • PromptLayer

Project

Call Center Transcript Sentiment Classifier

Build a production-grade sentiment + intent classifier for call center transcripts using structured output, function calling, and an evaluation harness to measure accuracy.

3. Week 3 - LLM SDK Mastery & RAG: Foundations to Production

Master SDK capabilities, build a full production RAG pipeline with hybrid search, reranking, RAGAS evaluation, and LangSmith observability.

Topics

  • Advanced SDK usage: streaming, async calls, retry logic, rate limiting
  • JSON schemas, connectors, and tool calls across OpenAI/Gemini/Anthropic
  • Why RAG is essential for enterprise AI — the knowledge freshness problem
  • End-to-end RAG pipeline architecture
  • Embeddings deep dive: how they work, how to choose the right model
  • Introduction to information retrieval: keyword (TF-IDF/BM25) vs semantic search
  • Vector databases: internals, HNSW, ANN algorithms
  • Chunking strategies: fixed, semantic, recursive, document-aware
  • Two-stage retrieval: retrieve-then-rerank with cross-encoders · RAGAS evaluation: faithfulness, relevance, completeness · LangSmith observability and tracing

Hand-ons

  • LangChain (RAG chains)
  • FAISS / ChromaDB
  • OpenAI Embeddings API
  • File Search SDK
  • RAGAS
  • LangSmith / LangFuse

Project

Company Knowledge-Base Chatbot

Complete Build a fully production-grade knowledge-base chatbot: ingest PDFs, websites, and proprietary data, implement hybrid search and reranking, evaluate with RAGAS, and trace with LangSmith

4. Week 4 - Building AI Agents

Build structured AI agents that reason, use tools, maintain memory, and handle failures gracefully.

Topics

  • Core components of an AI agent: roles, memory, tools, autonomy, goals
  • Agent types: Researcher, Writer, Planner, Analyzer — design patterns
  • ReAct framework: Reasoning + Acting in a loop
  • Chain-of-Thought vs ReAct vs Tool Use — when to use which
  • Tool calling deep dive: function schemas, tool orchestration, tool chaining
  • Memory systems: short-term (context), long-term (vector store), episodic
  • Connecting agents to APIs, databases, browsers, and file systems
  • Memory patterns and structured context passing
  • Autonomy levels and safe task-scoping

Hand-ons

  • LangChain Agents
  • Google ADK
  • OpenAI Function Calling
  • Anthropic Tool Use
  • Mem0 (memory)
  • Guardrails AI

Project

Researcher-and-Summarizer Agent + Customer Care Agent

Build two agents: (1) A Researcher-and-Summarizer that searches, reads, and synthesizes information autonomously. (2) A Customer Care Agent that uses tool calling to fetch live order information from.

5. Week 5 - Multi-Agent Systems & Google ADK

Design multi-agent architectures and build production coordinator patterns using CrewAI and Google ADK.

Topics:

  • Multi-agent architectures: sequential, parallel, hierarchical patterns
  • Role-based agents: specialist vs generalist design
  • Delegation logic: how agents assign and route sub-tasks
  • Coordinator–dispatcher architecture for production workflows
  • Passing context and outputs across agent boundaries
  • Background agents vs interactive agents
  • File persistence using structured Markdown and JSON reports
  • Building custom function tools: financial APIs, file writers, data fetchers
  • Designing scalable multi-agent orchestration patterns

Hand-ons

  • CrewAI
  • Google ADK
  • LangGraph (multi-agent)
  • Open AI SDK
  • OpenAI Assistants API
    • Model Context Protocol (MCP): what it is and why modern agents use it

Project

Financial News Research Coordinator

Build a Financial News Research Coordinator using Google ADK: a coordinator agent dispatches specialized sub-agents (news fetcher, sentiment analyzer, report writer) and produces a structured investment.

Week 6 : MCP, Voice, Multimodal, Evaluation & Capstone

Master MCP architecture, build voice G multimodal agents, evaluate and debug agentic systems, and deliver a fully integrated end-to-end Capstone project.

Evaluating agents end-to-end: task completion rate, tool use accuracy, response quality · Debugging agentic systems: tracing execution and identifying failure points · Reliability patterns: idempotency, retries, fallback strategies · Capstone architecture: RAG + Agents + Multi-Agent + MCP integrated.

Topics Covered:

  • MCP architecture: client, server, and transport layer
  • Registering tools, services, and capabilities through MCP servers
  • How MCP simplifies connecting agents to databases, APIs, and internal systems
  • MCP vs traditional tool-calling: comparison and migration path
  • Integrating MCP into multi-agent applications
  • Gemini multimodal capabilities: vision, audio, documents
  • Voice agent architecture: speech-to-text → LLM → action → text-to-speech Hand-ons Practical:
  • MCP SDK
  • Gemini Multimodal API
  • Google Speech-to-Text / TTS
  • LangSmith
  • Helicone
  • RAGAS · WGB · Guardrails AI · FastAPI Project:

MCP-Powered Agent + Voice Assistant + Capstone Build three deliverables:

  1. MCP server connecting agents to databases and APIs.
  2. Voice-enabled multimodal assistant.
  3. CAPSTONE: fully integrated multi-agent application combining RAG, tool- using agents, evaluation loops, and production deployment

Who Can Do

Ideal for
  • Early-career professionals with foundational Python knowledge
  • Software Engineers and Developers
  • Data Professionals
  • Project Manager
  • Marketing Professionals

FAQ

2. What are the benefits of doing this course?

You can expect to get a wide range of benefits from doing the Applied Agentic AI Certification training, these benefits will include:

Technical Benefits
  • Build Mini-ChatGPT from scratch (simplified implementation)
  • Create full RAG pipelines
  • Design and implement multi-agent systems
  • Learn decision frameworks (RAG vs Fine-tuning vs Prompting)
  • Gain production optimization skills (latency, cost, observability)
  • Work with 10+ industry-grade AI tools
  • Complete a real-world enterprise capstone project
3. How essential is this Agentic AI training?

This course is essential because:

  • AI is moving beyond chatbots to autonomous agent systems that reason and act.
  • Companies need engineers who can design complete AI systems, not just write prompts.
  • It teaches practical system design (architecture decisions, cost trade-offs, latency optimization).
  • You learn when to use prompting, RAG, or fine-tuning, a critical real-world skill.
  • It prepares you for emerging high-paying roles in AI engineering and GenAI systems.

In short, this course focuses on building real-world, production-ready AI agents, which aligns directly with current industry demand.

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