Agentic AI for Developers: Learn AI Agents & Automation

Name: Agentic AI for Developers Certification Training
Item: Agentic AI for Developers Certification Training
Rating: 5
Author: Sudha Kargi

Module 1: Introduction to Agentic AI

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

Hands on:

Analyzing AI Agent Use Cases
Exploring Agentic AI Frameworks

Skills you will learn:

Understanding Agentic AI Concepts
Identifying AI Agent Capabilities and Limitations
Navigating AI Frameworks and Architectures
Ethical and Responsible AI

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Module 2: Agentic AI: Architectures and Design Patterns

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

Hands-on:

Designing an AI agent architecture
Implementing different agentic AI design patterns

Skills you will Learn:

Understanding Agentic AI Frameworks
Implementing AI Design Patterns
Designing Secure and Scalable AI Architectures

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Module 3: Getting Started with LangChain and LCEL

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

Hands-on:

Build a Self-correcting Coding Assistant with LangChain

Skills you will Learn:

Data Processing with Langchain
AI-Powered Document Retrieval
Building AI Pipelines with LCEL

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Module 4: Building AI Agents with LangGraph

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

Hands-on:

Building a Finance Bot with LangGraph

Skills you will Learn:

State Management in AI Agents
Implementing Long-Term AI Memory
Deploying AI Agents with LangGraph

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Module 5: Implementing Agentic RAG

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

Hands-on:

Create an AI-Powered Sales Report Analyzer with LlamaIndex
Create a Market Research Agent with RAG & Cohere

Skills you will Learn:

Implementing RAG Techniques
Building AI Agents with LlamaIndex and Cohere
Optimizing AI Retrieval Systems

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Module 6: Developing AI Agents with Agno (Phidata)

Topics:

Agents
Models
Tools
Knowledge
Chunking
Vector DB
Storage
Embeddings
Workflows
Developing Agents with Phidata

Hands-on:

Design a Data Analysis Agent with Phidata

Skills you will Learn:

Building AI Agents with Phidata
Optimizing AI Workflows

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Module 7: Multi-Agent Systems with LangGraph and CrewAI

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

Hands-on:

Build a Customer Support Chatbot with LangGraph
Design a Stock Analysis Agent with CrewAI

Skills you will Learn:

Multi Agent Systems
Multi Agent Designs and Workflows

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Module 8: Agentic AI with Autogen

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

Hands-on:

Develop an AI Research Agent with Autogen

Skills you will Learn:

Building Adaptive AI Agents
Deploying AI Agents with Autogen

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Module 9: AI Agent Observability and AgentOPs

Topics:

Langfuse Overview
Langfuse Dashboard
Tracing
Evaluation
Managing Prompts
Experimentation
AI Observability with Langsmith
Setting up Langsmith
Managing Workflows with Langsmith
AgentOps Practical Implementation

Hands-on:

AI Observability with Langsmith
AgentOps Practical Implementation

Skills you will Learn:

Monitoring AI Agent Performance
Managing AI Workflows
Implementing AI Experimentation and Observability

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Module 10: Building AI Agents with Langflow and Relevance AI

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

Hands-on:

Content Writer Agent in Wordware
Design Your own SEO Agent with Relevance AI
Creating an AI Agent with Langflow

Skills you will Learn:

No-Code AI Development
Workflow Automation Using AI
Building and Deploying AI Agents

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Bonus Module: Generative and Agentic AI on Cloud (Self-paced)

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

Hands-on:

Build and Deploy AI Models on AWS Bedrock, Azure OpenAI, and GCP Vertex AI

Skills you will Learn:

Cloud-based AI Model Deployment of Generative AI Models
AI Agent Development on Cloud

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Agentic AI Training Course Overview and Key Features

The Agentic AI course enables learners to build autonomous AI agents using LLMs like GPT without coding. This course by Edureka offers hands-on projects and tools like LangChain, CrewAI, and AutoGen. It covers 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.

Key Features:

No-code AI agent development
Real-world LLM projects
Tools: LangChain, AutoGen, CrewAI
Capstone project with expert guidance

What are the prerequisites for this Agentic AI Training Course?

Basic Python, ML, DL, NLP, generative AI, and prompt engineering knowledge is needed. Refresher materials are provided before live sessions.

What will participants learn during this Course?

Upon completing the Agentic AI course, participants will learn:

Build autonomous AI agents with LangChain, LangGraph, and CrewAI.
Implement agentic RAG for smart retrieval.
Develop multi-agent AI systems.
Integrate Autogen for adaptive workflows.
Monitor AI with LangFuse observability.
Use no-code AI development tools.
Deploy on AWS, Azure, and GCP.
Complete hands-on projects for real-world skills.

Who should take this AI Agents Training Course?

The course is ideal for:

AI Enthusiasts and Developers
LLM Engineers & Generative AI Engineers
AI Research Scientists
AI/ML Practitioners
Freshers looking to enter AI roles
Professionals aiming to use Agentic AI for automation, reasoning, and decision-making

How long does it take to complete this course?

The Agentic AI Certification Course by Edureka takes approximately 5 weeks to complete. Classes are held on weekends (Saturdays and Sundays). Includes live instructor-led sessions for interactive learning. Offers a bonus self-paced module on Generative and Agentic AI on Cloud with 12 topics. The self-paced module lets learners explore advanced concepts at their own convenience.As AI grows more independent, understanding its ethical and safety implications becomes crucial, making expertise in this field highly valuable for the future.

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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:

Enables smart, adaptive autonomous systems
Advances in robotics and automation
Creates AI career opportunities
Raises awareness of AI ethics and safety
Prepares for the future of AI technology

What if I have questions after completing this 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 would be my role as an agentic AI Engineer?

As an agentic AI Engineer, you'll be responsible for

Design AI agents that perceive, decide, and act to meet goals
Use reinforcement learning, NLP, and planning techniques
Build autonomous agents that adapt to change

What are the features of agentic AI?

The key features of agentic AI are:

Autonomy: Operates independently with minimal human intervention
Perception: Senses and understands its environment
Decision-making: Makes intelligent choices based on data and goals
Adaptability: Learns and adjusts to changing conditions
Goal-oriented: Focuses actions on achieving specific objectives
Learning: Uses techniques like reinforcement learning to improve over time
Planning: Anticipates future states and strategizes accordingly

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.

How does the agentic AI online course contribute to career advancement in the AI field?

The agentic AI course enhances career advancement by

Enhance career advancement with specialized skills
Boosts professional credibility
Opens doors to high-demand AI roles
Expands career prospects in AI
Keeps professionals updated with AI advancements
Certification is a key asset for standing out in a competitive field
Supports 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 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 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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Agentic AI for Developers Certification Training

Why should you take Agentic AI for Developers?

AI Agents Course Curriculum

Module 1: Introduction to Agentic AI

Module 2: Agentic AI: Architectures and Design Patterns

Module 3: Getting Started with LangChain and LCEL

Module 4: Building AI Agents with LangGraph

Module 5: Implementing Agentic RAG

Module 6: Developing AI Agents with Agno (Phidata)

Module 7: Multi-Agent Systems with LangGraph and CrewAI

Module 8: Agentic AI with Autogen

Module 9: AI Agent Observability and AgentOPs

Module 10: Building AI Agents with Langflow and Relevance AI

Bonus Module: Generative and Agentic AI on Cloud (Self-paced)

Agentic AI Course Description

Agentic AI Training Course Overview and Key Features

What are the prerequisites for this Agentic AI Training Course?

What will participants learn during this Course?

Who should take this AI Agents Training Course?

How long does it take to complete this course?

Testimonial Reviews

Video Reviews

Frequently Asked Questions (FAQs)

What is Agentic AI?

Why learn Agentic AI?

What if I have questions after completing this online training?

What would be my role as an agentic AI Engineer?

What are the features of agentic AI?

Is ChatGPT, Gemini, Claude, or Deepseek an example of agentic AI?

How does the agentic AI online course contribute to career advancement in the AI field?

Can someone with minimal AI experience pursue the agentic AI course and find related job opportunities?

Is it worth learning Agentic AI?

Why should you become an Agentic AI Engineer?

Is AI Agent Engineer a good Career Option?

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