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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

Who Should Enroll in This Agentic AI Certification Course?

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

Explore our Schedules

Schedules
No Results
  • 500k+
    Career Transformations
  • 250+
    Workshops every month
  • 100+
    Countries and counting

Applied Agentic AI Highlights

Why Choose upGrad KnowledgeHut AI Agentic?

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.

You’ll learn:

  • Why AI sometimes gives wrong answers
  • How it remembers context
  • What makes responses better or worse
  • The difference between prompting, using external data, and training a model

Practical:

  • Build a mini GPT (simplified PyTorch implementation)
  • Generate embeddings
  • Implement cosine similarity search
  • Experiment with prompt variations

2. Week 2 - Advanced RAG Systems + Decision Framework

Design production-ready retrieval systems. End-to-end RAG pipeline, Chunking strategy comparison, Embedding selection logic, Vector DB internals (FAISS basics), Hybrid search concept (keyword + vector), RAG vs Fine-Tuning decision framework, Data availability, Cost implications, Latency trade-offs, and Maintenance complexity.

You’ll learn:

  • How to connect AI to custom data
  • How to structure documents for better answers
  • How to improve accuracy and reduce guesswork
  • When to use data retrieval vs model training

Practical:

  • Build full RAG pipeline
  • Implement 3 chunking strategies
  • Evaluate retrieval quality

Assignment

  • Benchmark chunking strategies + submit performance report with improvement recommendations.

3. Week 3 - Agent Architecture Deep Dive

Move from LLM app to reasoning system. ReAct vs Chain-of-Thought vs Tool Use, Memory systems (short-term vs long-term), Planning mechanisms, Failure handling strategies, and Guardrails introduction.

You’ll learn how to:

  • Make AI think step by step
  • Give it memory
  • Let it use tools
  • Handle mistakes more intelligently

Practical:

  • Build single-agent system
  • Add memory layer
  • Integrate tool usage
  • Implement basic logging

4. Week 4 - Multi-Agent Systems & Coordination

Design collaborative AI systems Role-based agents, Delegation logic, Coordination patterns, Conflict resolution, and Safety and guardrails systems No-code orchestration layer (tool-based demo).

You’ll learn how to design systems where:

  • Different AI agents have different roles
  • They collaborate and divide tasks
  • They work together to solve bigger problems

Practical:

Build:

  • Multi-agent research assistant 
    OR
  • Financial analysis agent system

5. Week 5 - Production Optimization + Applied Fine-Tuning

Engineer for performance, cost, and reliability. Latency measurement, Token cost estimation, Prompt robustness testing, Logging & observability, Hallucination detection, and Fine-Tuning Strategy (Industry Relevant).

You’ll learn how to:

  • Reduce response time
  • Control usage cost
  • Improve output reliability
  • Decide when to fine-tune a model

Practical

  • Format dataset for fine-tuning
  • Run lightweight fine-tune workflow (API or adapter concept)
  • Compare:
    • Prompt-only
    • RAG-based
    • Fine-tuned approach

Assignment

Optimize AI system for:

  • Cost efficiency
  • Latency reduction
  • Output quality improvement


6. Week 6 - Enterprise Capstone

Simulate real-world AI system design review - Design enterprise-grade AI agent system, Choose architecture, Write detailed architecture document.

AI Agentic Tools Covered

  • PyTorch
  • Ollama
  • OpenAI API
  • LangChain
  • CrewAI
  • FAISS
  • Chroma
  • n8n
  • Langfuse
  • FastAPI
Tools
Master Cutting Edge AI Tools

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.

Agentic-AI Roadmap
LLM Foundations
Build a mini GPT (simplified PyTorch implementation), Generate embeddings, Implement cosine similarity search, and Experiment with prompt variations.
Agent Architecture Deep Dive
Build single-agent system, Add memory layer, Integrate tool usage, and Implement basic logging
Production Optimization
Format dataset for fine-tuning and Run fine-tune workflow (API or adapter concept)
Congratulations
On your new job role!
Steps to Agentic AI Certification
Advanced RAG Systems
Build full RAG pipeline, Implement 3 chunking strategies, and Evaluate retrieval quality
Multi-Agent Systems
Multi-agent research assistant / Financial analysis agent system
Enterprise Capstone
Design enterprise-grade AI agent system

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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