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Best Agentic AI Course with Certification (Top #1)

Applied Agentic AI Certification

Build Autonomous AI Systems That Think, Plan, and Execute

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Eligibility Criteria for Applied Agentic AI Certification

Prerequisites and Eligibility

You don’t need to be an AI or technical expert to join this program.
Not sure if you’re ready?

No worries. We provide easy-to-follow refresher materials before the live sessions to help you get up to speed.

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

Prerequisites image
  • 500k+
    Career Transformations
  • 250+
    Workshops every month
  • 100+
    Countries and counting

Who Should Enroll in This Agentic AI Course?

Who is this Course for
  • Early-career professionals with foundational Python knowledge
  • Software Engineers and Developers
  • Data Professionals
  • Project Manager
  • Marketing Professionals
Whoshouldlearn image

Applied Agentic AI Training Highlights

Course Highlights

36 Contact Hours with Live, Instructor-Led Sessions designed to help you understand and build real-world AI systems step by step.

Get Complementary Python Fundamentals Self-Paced Course and refresh your Python skills with training designed for AI system development.

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.

Enterprise Capstone Project focused on designing and presenting an enterprise-level AI agent system with detailed architecture documentation.

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Why Choose upGrad KnowledgeHut for Agentic AI Course?

upGrad KnowledgeHut Edge

Personalized 1:1 mentorship

to help you think like an AI engineer and not just code like one

Strategic career support

focused on positioning, interview readiness, and real hiring outcomes

Hands-on mastery

of 10+ industry-grade AI tools used in production environments

Build a production-ready AI system

you can showcase as proof of real capability

Lifetime access to live sessions

so your learning never stops as the industry evolves
Contact Learning Advisor
Ready to unlock your full potential as an Agentic AI Practitioner?

Applied Agentic AI Certification 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

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.

Tools
Master Cutting Edge AI Tools
Agentic-AI Roadmap
LLM Foundations
Build a mini GPT, generate embeddings, implement cosine search, test prompts.
LLM SDK Mastery and RAG
Master SDKs, build production RAG with hybrid search, reranking, RAGAS, and LangSmith.
Multi-Agent Systems
Design multi-agent systems and production coordinators with CrewAI and Google ADK.
Congratulations
On your new job role!
Steps to Agentic AI Certification
Prompt Engineering
Master advanced prompting and build systems for reliable, structured, evaluable outputs.
Building AI Agents
Build structured AI agents that reason, use tools, maintain memory, and handle failures gracefully.
Capstone
Master MCP, build voice and multimodal agents, debug systems, deliver full capstone.

Demand for Applied Agentic AI Professionals

Career Outcomes That Matter
Annual Salary
Min
Average
Max
Hiring Companies
Oracle
Accenture
Bank of America
Bosch
Abbot
Allianz
AI Adoption
78%
Companies globally have integrated AI at least partially

The job market for Agentic AI is expanding at a remarkable pace, driven by rapid enterprise adoption and a widening talent gap. Demand for agentic and advanced AI roles is projected to grow by 35–40% annually, while the supply of qualified professionals remains over 50% below market demand, creating strong opportunities for skilled talent.

Globally, AI and automation are forecast to generate a net gain of 78 million jobs by 2030, and professionals with AI skills earn 20–28% higher salaries on average compared to peers without AI expertise. As AI job postings have grown from 1.4% to 9.5% of tech listings over the past decade, it’s clear that Agentic AI is not just a trend, it represents a major long-term career growth opportunity.

GET THE APPLIED AGENTIC AI CERTIFICATION

Earn the Coveted Applied Agentic AI Certification

Applied Agentic AI Certificate
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Applied Agentic AI Certification FAQs

Frequently Asked Questions
Agentic AI Training

1. What is this course all about?

The Applied Agentic AI Certification is a 6-week program designed to help you build autonomous AI systems that think, plan, and execute.

The course focuses on:

  • Understanding LLM foundations (transformers, attention, embeddings, hallucinations)
  • Building real systems like Mini-ChatGPT
  • Designing and deploying RAG (Retrieval-Augmented Generation) pipelines
  • Developing single-agent and multi-agent AI systems
  • Implementing memory, planning, tool usage, and guardrails
  • Fine-tuning models for performance, cost, and reliability
  • Designing enterprise-grade AI agent architectures
  • It is highly hands-on and project-driven, covering tools like PyTorch, OpenAI API, LangChain, FAISS, CrewAI, FastAPI, and more.

By the end of the course, you simulate a real-world enterprise AI system design review and build a production-ready AI agent system.

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

Career Benefits

  • 1:1 mentorship
  • Career support
  • Lifetime live session access
  • Industry-relevant assignments
  • Preparation for roles such as:
    • AI Engineer
    • Gen AI Engineer
    • LLM Engineer
    • Prompt Engineer

The course is structured to help you move from learning concepts to deploying industry-grade AI agents.

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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