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...
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.
To get the most out of the course, you should:
designed to help you understand and build real-world AI systems step by step.
to build practical solutions such as a Mini-GPT model, a RAG pipeline, and both single-agent and multi-agent AI architectures.
including hands-on use of production-ready tools such as Claude.ai, LangChain, CrewAI, LangGraph, Chroma, FastAPI, Ollama, and more.
to learn how to connect AI models with real-world data using embeddings, vector databases, chunking strategies, and hybrid search.
focused on building AI agents that reason, use tools effectively, collaborate, and handle complex problems.
to enhance AI systems for better performance, cost efficiency, and reliability while deploying fine-tuned models in real-world applications.
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
Hand-ons
Project
Multi-SDK LLM Playground
Build a unified interface that queries OpenAI, Gemini, and Claude simultaneously and compares responses side by side.
Master advanced prompting techniques and build systems that produce reliable, structured, evaluable outputs.
Topics
Hand-ons
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.
Master SDK capabilities, build a full production RAG pipeline with hybrid search, reranking, RAGAS evaluation, and LangSmith observability.
Topics
Hand-ons
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
Build structured AI agents that reason, use tools, maintain memory, and handle failures gracefully.
Topics
Hand-ons
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.
Design multi-agent architectures and build production coordinator patterns using CrewAI and Google ADK.
Topics:
Hand-ons
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-Powered Agent + Voice Assistant + Capstone Build three deliverables:
You can expect to get a wide range of benefits from doing the Applied Agentic AI Certification training, these benefits will include:
This course is essential because:
In short, this course focuses on building real-world, production-ready AI agents, which aligns directly with current industry demand.
