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Agentic AI Training Course - Master AI Agents
Agentic AI Training Course - Master AI Agents
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Live Online Classes starting on 26th Apr 2025
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Why enroll for Agentic AI Training Course ?
AI Agents Training Course Benefits
The Agentic AI market is anticipated to grow at a CAGR of 45.8% from 2025 to 2030, with 90% of businesses anticipating it to be crucial to their future success. As businesses adopt Agentic AI, the demand for experts in autonomous AI systems is soaring. Our program empowers you with cutting-edge expertise to thrive in this rapidly evolving field at the forefront of AI innovation.
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Why Agentic AI Training Course from edureka
Live Interactive Learning
- World-Class Instructors
- Expert-Led Mentoring Sessions
- Instant doubt clearing
Lifetime Access
- Course Access Never Expires
- Free Access to Future Updates
- Unlimited Access to Course Content
24x7 Support
- One-On-One Learning Assistance
- Help Desk Support
- Resolve Doubts in Real-time
Hands-On Project Based Learning
- Industry-Relevant Projects
- Course Demo Dataset & Files
- Quizzes & Assignments
Industry Recognised Certification
- Edureka Training Certificate
- Graded Performance Certificate
- Certificate of Completion
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About your Agentic AI Training Course
Skills Covered in this AI Agents online Course
Agentic AI Tools Covered
Agentic AI Training Course Curriculum
Curriculum Designed by Experts
Agentic AI Essentials
11 Topics
Topics
- Agentic AI Introduction
- AI Agents vs. Agentic AI
- Comparison: Agentic AI, Generative AI, and Traditional AI
- Agentic AI Building Blocks
- Autonomous Agents
- Human in the Loops Systems
- Single and Multi Agent AI Systems
- Agentic AI Frameworks Overview
- Ethical and Responsible AI
- Agentic AI Best Practices
- AI Implementation Success Stories: Case Studies
![skill]( "Hands On Experience")
- Analyzing AI Agent Use Cases
- Exploring Agentic AI Frameworks
![skill]( "skill you will learn")
- Understanding Agentic AI Concepts
- Identifying AI Agent Capabilities and Limitations
- Navigating AI Frameworks and Architectures
- Ethical and Responsible AI
Agentic AI: Architectures and Design Patterns
12 Topics
Topics
- Agentic AI Architecture
- Agentic Architecture Types
- Key Components of the Agentic AI Framework
- Perception Module
- Cognitive Module
- Action Module
- Learning Module
- Collaboration Module
- Security Module
- Agentic AI Design Patterns
- Reflection Pattern
- Tool Use Pattern
- Planning Pattern
- ReAct (Reasoning and Acting) and ReWOO (Reasoning with Open Ontology)
- Multi Agent Pattern
- Design Considerations
![skill]( "Hands On Experience")
- Designing an AI agent architecture
- Implementing different agentic AI design patterns
![skill]( "skill you will learn")
- Understanding Agentic AI Frameworks
- Implementing AI Design Patterns
- Designing Secure and Scalable AI Architectures
Working with LangChain and LCEL
10 Topics
Topics
- Components and Modules
- Data Ingestion and Document Loaders
- Text Splitting
- Embeddings
- Integration with Vector Databases
- Introduction to Langchain Expression Language (LCEL)
- Runnables
- Chains
- Building and Deploying with LCEL
- Deployment with Langserve
![skill]( "Hands On Experience")
- Build a Resume Screening Application with LangChain
- Develop a Legal Document Review Application with LangChain
![skill]( "skill you will learn")
- Data Processing with Langchain
- AI-Powered Document Retrieval
- Building AI Pipelines with LCEL
Building AI Agents with LangGraph
13 Topics
Topics
- Introduction to LangGraph
- State and Memory
- State Schema
- State Reducer
- Multiple Schemas
- Trim and Filter Messages
- Memory and External Memory
- UX and Human-in-the-Loop (HITL)
- Building Agent with LangGraph
- Long Term Memory
- Short vs. Long Term Memory
- Memory Schema
- Deployment
![skill]( "Hands On Experience")
- Building a Finance Bot with LangGraph
![skill]( "skill you will learn")
- State Management in AI Agents
- Implementing Long-Term AI Memory
- Deploying AI Agents with LangGraph
Implementing Agentic RAG
8 Topics
Topics
- What is Agentic RAG?
- Agentic RAG vs. Traditional RAG
- Agentic RAG Architecture and Components
- Understanding Adaptive RAG
- Variants of Agentic RAG
- Applications of Agentic RAG
- Agentic RAG with Llamaindex
- Agentic RAG with Cohere
![skill]( "Hands On Experience")
- Create an AI-Powered Sales Report Analyzer with LlamaIndex
- Create a Market Research Agent with RAG & Cohere
![skill]( "skill you will learn")
- Implementing RAG Techniques
- Building AI Agents with LlamaIndex and Cohere
- Optimizing AI Retrieval Systems
Developing AI Agents with Phidata
10 Topics
Topics
- Agents
- Models
- Tools
- Knowledge
- Chunking
- Vector DB
- Storage
- Embeddings
- Workflows
- Developing Agents with Phidata
![skill]( "Hands On Experience")
- Design a Data Analysis Agent with Phidata
![skill]( "skill you will learn")
- Building AI Agents with Phidata
- Optimizing AI Workflows
Multi Agent Systems with LangGraph and CrewAI
9 Topics
Topics
- Multi Agent Systems
- Multi Agent Workflows
- Collaborative Multi Agents
- Multi Agent Designs
- Multi Agent Workflow with LangGraph
- CrewAI Introduction
- CrewAI Components
- Setting up CrewAI environment
- Building Agents with CrewAI
![skill]( "Hands On Experience")
- Building Multi Agent Systems with LangGraph and CrewAI
![skill]( "skill you will learn")
- Build a Customer Support Chatbot with LangGraph
- Design a Stock Analysis Agent with CrewAI
Advanced Agent Development with Autogen
10 Topics
Topics
- Autogen Introduction
- Salient Features
- Roles and Conversations
- Chat Terminations
- Human-in-the-Loop
- Code Executor
- Tool Use
- Conversation Patterns
- Developing Autogen-powered Agents
- Deployment and Monitoring
![skill]( "Hands On Experience")
- Develop an AI Research Agent with Autogen
![skill]( "skill you will learn")
- Building Adaptive AI Agents
- Deploying AI Agents with Autogen
AI Agent Observability and AgentOPs
10 Topics
Topics
- AI Agent Observability and AgentOPs
- Langfuse Dashboard
- Tracing
- Evaluation
- Managing Prompts
- Experimentation
- AI Observability with Langsmith
- Setting up Langsmith
- Managing Workflows with Langsmith
- AgentOps Practical Implementation
![skill]( "Hands On Experience")
- AI Observability with Langsmith
- AgentOps Practical Implementation
![skill]( "skill you will learn")
- Monitoring AI Agent Performance
- Managing AI Workflows
- Implementing AI Experimentation and Observability
Building AI Agents with No/Low- Code Tools
12 Topics
Topics
- Introduction to No-Code/Low-Code AI
- Benefits and Challenges of No-Code AI Development
- Key Components of No-Code AI Platforms
- Building AI Workflows Without Coding
- Designing AI Agents with Drag-and-Drop Interfaces
- Integrating No-Code AI with Existing Systems
- Customizing and Fine-Tuning AI Solutions
- Optimizing Performance and Efficiency in No-Code AI
- Security and Compliance Considerations in No-Code AI
- Best Practices for Deploying No-Code AI Solutions
- Real-World Use Cases and Applications of No-Code AI
- Scaling and Future Trends in No-Code AI
![skill]( "Hands On Experience")
- Scaling and Future Trends in No-Code AI
- Design Your own SEO Agent with Relevance AI
- Creating an AI Agent with Langflow
![skill]( "skill you will learn")
- No-Code AI Development
- Workflow Automation Using AI
- Building and Deploying AI Agents
Bonus Module: Generative and Agentic AI on Cloud (Self-paced)
12 Topics
Topics
- Deploying Generative AI Models with Amazon Bedrock
- Implementing Retrieval-Augmented Generation (RAG)
- Building and Managing AI Agents
- Serverless AI Agent Deployment
- Observability and Monitoring AI Agents
- Developing Generative AI Applications with Azure OpenAI Service
- Implementing Agentic AI Workflows with Azure Machine Learning (AML)
- Fine-Tuning Large Language Models (LLMs) on Azure
- Building AI Agents on Azure
- AI Model Deployment and Governance on Azure
- Working with Vertex AI Agent Builder
- Building No Code Conversational AI Agents
![skill]( "Hands On Experience")
- Build and Deploy AI Models on AWS Bedrock, Azure OpenAI, and GCP Vertex AI
![skill]( "skill you will learn")
- Cloud-based AI Model Deployment of Generative AI Models
- AI Agent Development on Cloud
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Agentic AI Online Course Details
About Agentic AI Training Course
This Agentic AI course enables learners to master agentic AI and build state-of-the-art AI agents. Participants will learn about the essential concepts of agentic AI, agentic AI design patterns and architecture, agentic RAG, Building AI agents with different frameworks, AI observability and monitoring, and using no/low code tools for building agents.
You will also have the opportunity to go through a bonus self-paced module which will help you master generative and agentic AI on cloud through different cloud platforms such as AWS, Azure, and GCP.
What is Agentic AI?
Agentic AI refers to artificial intelligence systems that can autonomously make decisions, take actions, and pursue goals with minimal human intervention. Unlike traditional AI, which adheres to predetermined rules, agentic AI may dynamically adapt to new situations. It is widely utilized in robotics, virtual assistants, self-driving cars, and sophisticated decision-making processes.
Why learn Agentic AI?
Learning Agentic AI is essential for building autonomous systems that make intelligent decisions, adapt to changes, and optimize tasks with minimal human input. It drives advancements in robotics, automation, and AI-powered assistants, opening career opportunities in AI research and development.
As AI grows more independent, understanding its ethical and safety implications becomes crucial, making expertise in this field highly valuable for the future.
What if I miss a live class of this agentic AI course?
You will have access to recorded sessions that you can review at your convenience.
What if I have queries after I complete this agentic AI online training?
You can reach out to Edureka’s support team for any queries, and you’ll have access to the community forums for ongoing help.
What are the prerequisites for this Agentic AI Training Course?
In order to complete this course successfully, participants need to have a basic understanding of Python programming language, machine learning, deep learning, natural language processing, generative AI, and prompt engineering concepts. However, learners will be provided with self-learning refresher material on generative AI and prompt before beginning with this live classes of the AI agents course.
What will participants learn during the Agentic AI online Course?
Upon completing the agentic AI Training course, participants will learn to design and build autonomous AI agents using frameworks like LangChain, LangGraph, and CrewAI. They will implement agentic RAG for intelligent retrieval, develop multi-agent systems, and integrate Autogen for adaptive AI workflows.
The course covers AI observability with LangFuse, no-code AI development, and cloud deployment on AWS, Azure, and GCP. Hands-on projects ensure practical expertise in building scalable, real-world AI solutions.
What would be my role as an agentic AI Engineer?
As an agentic AI Engineer, you'll be responsible for designing and developing intelligent agents that can perceive their environment, make decisions, and take actions to achieve specific goals. This involves applying techniques like reinforcement learning, NLP, and planning to create AI agents that can operate autonomously and adapt to changing situations.
How soon after signing up would I get access to the learning content?
Once you sign up, you will get immediate access to the course materials and resources.
Who should take this AI Agents Training Course?
The Agentic AI online Training Course is ideal for AI enthusiasts, developers, and professionals looking to build autonomous AI agents. It is best suited for LLM Engineer, Generative AI Engineer, AI Research scientists, AI/ML practitioners, and freshers who want to leverage Agentic AI for automation and decision-making.
Is the course material accessible to the students even after the agentic AI course is over?
Yes, you will have lifetime access to the course material and resources, including updates.
Is AI Agent Engineer a good Career Option?
Yes, becoming an AI Agent Engineer is a very promising career path due to the rapidly growing field of agentic AI, which is viewed as a significant technological advancement with enormous potential across industries, making it a high-demand area for skilled professionals with the ability to build and deploy autonomous AI systems.
How will I execute the practicals in this Agentic AI Course?
A step-by-step guide for setting up environments and building AI agents will be provided in the Learning Management System (LMS).
Edureka's Support Team will be available 24/7 to assist you in case you have any questions or face any technical issues during the practicals.
What programming languages, tools, and frameworks are most relevant for preparing for the AI Agents Course?
To prepare for the AI agents certification, proficiency in Python is essential, along with tools like RAG, Vector Databases (Pinecone, Weaviate), Embedding Models, and API Integrations.
How can beginners learn Agentic AI ?
To learn agentic AI as a beginner, start by building a foundation in AI concepts like Python programming, machine learning and reinforcement learning, deep learning, NLP, generative AI, and prompt engineering. Gradually explore key agentic AI principles, including autonomy, goal-oriented behavior, and tool use. Hands-on experience with frameworks like LangChain, LangGraph, and OpenAI Gym will enhance learning. Completing real-world projects will help develop practical skills in building intelligent AI agents.
What are the system requirements for this Agentic AI Training Course?
The system requirements for this Agentic AI Training Course include:
Hardware Requirements:
- CPU: Multi-core processor (minimum 2 cores, 4 or more recommended)
- Memory (RAM): At least 8 GB (16 GB recommended for better performance)
- Storage: Minimum 30 GB of free disk space
- GPU is Optional but recommended
Software Requirements:
- Operating System: Windows, macOS, or Linux (Ubuntu 20.04+ recommended for AI development)
- Programming Language: Proficiency in Python (required)
- Development Tools: Jupyter Notebook or VS Code
- Package Management: Experience with pip, conda, or venv
What are the other AI courses available in Edureka?
Edureka has been offering top-trending AI courses like PG program on Gen AI and ML, Prompt Engineering , and Gen AI Masters Program. You can check out our AI Course Page.
Projects
Build a Self-correcting Coding Assistant with LangChain
Develop a coding assistant using LangChain that identifies and corrects errors in code snippets automatically. Implement AI-driven error detection and self-correction to enhance ....Building a Finance Bot with LangGraph
Create an AI-powered finance assistant that provides real-time financial insights and recommendations using LangGraph.Build a Resume Screening Application with LangChain
Develop an AI-powered resume screening tool that extracts and ranks key candidate information using LangChain components.Create an AI-Powered Sales Report Analyzer with LlamaIndex
Build an AI tool that analyzes sales reports, extracts key insights, and generates summaries using LlamaIndex.Design a Data Analysis Agent with Phidata
Build an AI-powered data analysis agent using Phidata to process and visualize structured and unstructured data.Build a Customer Support Chatbot with LangGraph
Design and deploy a multi-agent chatbot for customer support using LangGraph to handle multiple inquiries efficiently.Design a Stock Analysis Agent with CrewAI
Develop an AI agent that analyzes stock trends, retrieves financial news, and provides investment insights using CrewAI.Develop an AI Research Agent with Autogen
Create an AI agent that autonomously researches topics, synthesizes information, and generates structured reports using Autogen.Design Your Own SEO Agent with Relevance AI
Create an SEO-optimized content generator using Relevance AI that analyzes keywords and suggests improvements.Content Writer Agent in Wordware
Create an SEO-optimized content generator using Relevance AI that analyzes keywords and suggests improvements.Agentic AI Training Course Certification
Upon successful completion of the AI agents training, Edureka provides the course completion certificate, which is valid for a lifetime.
To unlock Edureka’s agentic AI course completion certificate, you must ensure the following:
Fully participate in the agentic AI Course and complete all modules.
Complete the quizzes and hands-on projects listed in the curriculum.
Earning an agentic AI certification validates expertise in building autonomous AI agents, showcasing proficiency in frameworks like LangChain and LangGraph. It enhances career growth, opening opportunities in AI, automation, and decision-making roles across industries such as finance, healthcare, and robotics. Additionally, it provides a competitive edge and emphasizes ethical AI practices, ensuring responsible AI development and deployment.
After earning the agentic AI Certification, you can pursue roles such as AI/ML Engineer, AI Research Scientist, Generative AI Engineer, LLM Engineer, AI Agent Engineer, Autonomous Systems Developer, and AI Solutions Architect. This certification enhances career growth in AI development, automation, and intelligent decision-making systems.
Agentic AI certification can be challenging as it requires a solid understanding of AI principles, autonomous systems, and AI agent frameworks like LangChain and LangGraph. However, with dedication, hands-on practice, and structured learning, it is achievable. Enrolling in an agentic AI course with hands-on projects and expert guidance can significantly enhance your preparation and mastery of AI agent development.
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Frequently Asked Questions (FAQs)
What are the features of agentic AI?
Agentic AI features include autonomy, allowing AI agents to operate independently, and goal-oriented behavior, enabling them to adapt their actions toward specific objectives. They exhibit context awareness, planning, and reasoning, ensuring efficient task execution. With memory and learning, agents improve over time and can collaborate in multi-agent systems. They integrate with tools and APIs, support human-in-the-loop oversight, enable adaptive decision-making, and prioritize security and governance for responsible AI usage.
Is ChatGPT, Gemini, Claude, or Deepseek an example of agentic AI?
No, ChatGPT, Gemini, Claude, and Deepseek are not fully agentic AI on their own. They are large language models (LLMs) designed for generative AI tasks like text generation, summarization, and answering questions.
However, when integrated with agentic AI frameworks like LangChain, AutoGen, or CrewAI, these models can exhibit agent-like behavior by performing autonomous tasks, using tools, and making goal-driven decisions.
What skills will I acquire upon completing the agentic AI training course?
Upon completing the agentic AI training course, you will gain skills in building autonomous AI agents, implementing multi-agent systems, using LangChain, LangGraph, and AutoGen, integrating AI tools and memory, deploying AI on cloud platforms, and ensuring ethical AI development.
How does the agentic AI online course contribute to career advancement in the AI field?
The agentic AI course enhances career advancement by providing specialized skills, boosting credibility, and opening doors to high-demand AI roles. It expands career prospects and ensures professionals stay updated with AI advancements. This certification is a key asset for standing out in the competitive AI field and achieving long-term career growth.
Can someone with minimal AI experience pursue the agentic AI course and find related job opportunities?
Yes, individuals with minimal AI experience can pursue the agentic AI online training, especially if they have a basic understanding of AI concepts and programming fundamentals. Prior experience with Python, AI workflows, and cloud platforms can be beneficial.
This certification helps professionals build expertise in AI agent development, autonomous systems, and multi-agent collaboration, unlocking career opportunities in roles such as LLM Engineer, Generative AI Engineer, AI Research scientists, and AI/ML practitioners. Having skills in emerging technologies like Autonomous AI Agents can further impact salary growth and job opportunities in this field.
Is becoming an AI agent engineer a promising career path?
Yes, becoming an AI Agent Engineer is a highly promising career path as autonomous AI systems are transforming industries. With growing demand for intelligent agents, AI-powered decision-making, and multi-agent collaboration, professionals skilled in agentic AI frameworks can secure high-paying roles making it a future-proof and rewarding career choice.
What is the future scope of agentic AI in the AI and automation industry?
Agentic AI has a transformative future in AI and automation, enabling autonomous systems like self-driving vehicles, smart manufacturing, and personalized healthcare. Its ability to handle complex tasks with minimal human intervention will drive efficiency, cost savings, and innovation across industries, making it a cornerstone of advanced automation and intelligent systems in the coming years.
What are the examples of Agentic AI?
Examples of Agentic AI include AutoGPT and BabyAGI, which autonomously generate and execute tasks. CrewAI enables AI agents to collaborate on complex workflows, while LangGraph Agents use stateful memory for decision-making. Autogen facilitates multi-agent interactions with human oversight. LlamaIndex enhances document retrieval in AI workflows, while Pinecone, Weaviate, and ChromaDB serve as vector databases for efficient AI memory and knowledge retrieval.
How is Agentic AI different from generative AI?
Agentic AI focuses on autonomous decision-making, reasoning, and action execution, enabling AI agents to operate independently and collaborate dynamically. In contrast, Generative AI specializes in creating content, such as text, images, or code, based on learned patterns. While Generative AI generates outputs from prompts, agentic AI actively engages with environments, utilizes tools, plans actions, and adapts over time, making it more interactive and goal-driven than purely content-generating AI.
Is it worth learning Agentic AI?
Yes, learning Agentic AI is likely worth it, as a rapidly growing field with significant potential to revolutionise how businesses operate by automating complex tasks, enabling autonomous decision-making, and facilitating seamless human-AI collaboration, it is regarded as a valuable skill for the future of work across a wide range of industries.
Why should you become an Agentic AI Engineer?
Becoming an agentic AI engineer is beneficial for the participant as it is transforming the business operations, enhancing productivity, and redefining innovation.
Is Agentic AI the future?
Yes, Agentic AI is shaping the future of AI by enabling autonomous, goal-driven systems that can reason, plan, and act independently. Unlike traditional AI, which follows predefined rules, Agentic AI dynamically adapts, collaborates, and interacts with its environment. As industries move toward automation, intelligent decision-making, and multi-agent collaboration, Agentic AI will play a crucial role in revolutionizing business processes, AI-powered assistants, robotics, and real-world problem-solving.
Is AI Agent Engineer a good Career Option?
Yes, becoming an AI Agent Engineer is a very promising career path due to the rapidly growing field of agentic AI, which is viewed as a significant technological advancement with enormous potential across industries, making it a high-demand area for skilled professionals with the ability to build and deploy autonomous AI systems.
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