Agentic AI for Developers Certification Training





Instructor-led Agentic AI for Developers live online Training Schedule
Flexible batches for you
Why enroll for Agentic AI for Developers?



AI Agents for Developer Coures Benefits




Why Agentic AI for Developers from edureka
Live Interactive Learning
- World-Class Instructors
- Expert-Led Mentoring Sessions
- Instant doubt clearing
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 for Developers
Skills Covered
Agentic AI Development AI Architecture Design Agentic RAG Implementation Multi-agent Systems AI Observability & Ops Building No/ Low Code AI Agents
Agentic AI Tools Covered
Agentic AI for Developer Curriculum
Curriculum Designed by Experts
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 Experience skill]()
Hands-on
- Analyzing AI Agent Use Cases
- Exploring Agentic AI Frameworks
![skill you will learn skill]()
Skills
- 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
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 Experience skill]()
Hands-on
- Designing an AI agent architecture
- Implementing different agentic AI design patterns
![skill you will learn skill]()
Skills
- Understanding Agentic AI Frameworks
- Implementing AI Design Patterns
- Designing Secure and Scalable AI Architectures
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 Experience skill]()
Hands-on
- Build a Resume Screening Application with LangChain
- Develop a Legal Document Review Application with LangChain
![skill you will learn skill]()
Skills
- Data Processing with Langchain
- AI-Powered Document Retrieval
- Building AI Pipelines with LCEL
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 Experience skill]()
Hands-on
- Building a Finance Bot with LangGraph
![skill you will learn skill]()
Skills
- State Management in AI Agents
- Implementing Long-Term AI Memory
- Deploying AI Agents with LangGraph
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 Experience skill]()
Hands-on
- Create an AI-Powered Sales Report Analyzer with LlamaIndex
- Create a Market Research Agent with RAG & Cohere
![skill you will learn skill]()
Skills
- Implementing RAG Techniques
- Building AI Agents with LlamaIndex and Cohere
- Optimizing AI Retrieval Systems
Developing AI Agents with Agno (Phidata)
Topics
- Agents
- Models
- Tools
- Knowledge
- Chunking
- Vector DB
- Storage
- Embeddings
- Workflows
- Developing Agents with Phidata
![Hands On Experience skill]()
Hands-on
- Design a Data Analysis Agent with Phidata
![skill you will learn skill]()
Skills
- Building AI Agents with Phidata
- Optimizing AI Workflows
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 Experience skill]()
Hands-on
- Building Multi Agent Systems with LangGraph and CrewAI
![skill you will learn skill]()
Skills
- Build a Customer Support Chatbot with LangGraph
- Design a Stock Analysis Agent with CrewAI
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 Experience skill]()
Hands-on
- Develop an AI Research Agent with Autogen
![skill you will learn skill]()
Skill
- Building Adaptive AI Agents
- Deploying AI Agents with Autogen
AI Agent Observability and AgentOPs
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
![Hands On Experience skill]()
Hands-on
- AI Observability with Langsmith
- AgentOps Practical Implementation
![skill you will learn skill]()
Skills
- Monitoring AI Agent Performance
- Managing AI Workflows
- Implementing AI Experimentation and Observability
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 Experience skill]()
Hands-on
- Scaling and Future Trends in No-Code AI
- Design Your own SEO Agent with Relevance AI
- Creating an AI Agent with Langflow
![skill you will learn skill]()
Skills
- No-Code AI Development
- Workflow Automation Using AI
- Building and Deploying AI Agents
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 Experience skill]()
Hands-on
- Build and Deploy AI Models on AWS Bedrock, Azure OpenAI, and GCP Vertex AI
![skill you will learn skill]()
Skills
- Cloud-based AI Model Deployment of Generative AI Models
- AI Agent Development on Cloud
Agentic AI for Developer Description
What is Edureka's Agentic AI for Developer Training Course?
The 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 for Developer Training Course?
What will participants learn during this Course?
Upon completing the 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 for Developer 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
What is the duration of the Agentic AI for Developer?
What skills will I learn in the course?
You will develop practical skills for designing, building, and deploying AI agents. Key skills include:
- LangChain, LangGraph, and CrewAI
- MCP and tool integration
- Agentic RAG and GraphRAG
- Multi-agent orchestration
- AI guardrails and observability
- FastAPI, Docker, and deployment
What is the Cost of this Course?
Projects
Agentic AI for Developer Certification
To unlock Edureka’s course completion certificate, you must ensure the following:
Fully participate in the Course and complete all modules.
Complete the quizzes and hands-on projects listed in the curriculum.
Earning an Agentic AI for Developer certification offers multiple career and industry benefits, including:
Validates skills in building autonomous AI agents with LangChain and LangGraph.
Boosts career opportunities in AI and automation.
Relevant for finance, healthcare, and robotics industries.
Gives a competitive job market edge.
Highlights ethical and responsible AI development.
After earning the 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.
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Edureka Agentic AI for Developer Advantage
| WHAT I NEED | EDUREKA ADVANTAGE | WHAT OTHERS OFFER |
|---|---|---|
| Agentic-AI focused curriculum | ||
| Multi-Agent Systems training | ||
| RAG pipeline implementation | ||
| Tool-using AI agents | ||
| Observability (AgentOps) | ||
| Real-world agent projects | ||
| Instructor-led live classes | ||
| Portfolio-ready assignments | ||
| Career transition readiness |
Frequently Asked Questions (FAQs)
What is Agentic AI?
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?
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 for Developer online course contribute to career advancement in the AI field?
The 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


