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Applied Agentic AI Course

Prerequisites and EligibilityTo get the most out of the course, you should:Be comfortable writing basic Python codeUnderstand simple concepts like loops and functionsHave some idea of how APIs connect applications (nice to have, not required) Who is this Course forEarly-care...

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

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Join professionals upskilling with Edutech.

$999
  • Duration 18 Weeks
  • Format Live + Recorded
  • 4 Portfolio Projects
  • Rated 4.9 / 5
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Why Edutech?

  • Weekly mentorship checkpoints
  • Portfolio-grade capstone review
  • Interview acceleration toolkit

Overview

Prerequisites and Eligibility

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)

 

Who is this Course for

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

Course Highlights(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

Curriculum

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

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

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

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

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

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:

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

 

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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