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Become a GEN-AI & Agentic AI Developer : The Future of Learning is here.

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Step into the future of intelligence with hands-on training in Generative AI and Agentic AI agents. Learn to design and deploy AI systems that understand, plan, and act like digital experts.

  • Master Prompt Engineering
  • Learn LLMs, RAG & MCP
  • Work with Frameworks like LangChain, AutoGen, CrewAI
  • Deploy web applications using modern tools
  • 12 Weeks of Intensive Online Training

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GEN AI & AGENTIC AI Developer Virtual training in Hyderabad - Course Details

Step into the future of intelligence with hands-on training in Gen AI & Agentic AI Developer Virtual training. This program teaches you to build real-world AI systems using industry-leading tools like OpenAI, Anthropic, Gemini, LangChain, LlamaIndex, and advanced RAG pipelines. You’ll learn to design smart agents capable of reasoning, planning, and executing tasks across APIs, databases, and business workflows. Our curriculum covers no-code to pro-code frameworks such as Make.com, n8n, Python, FastAPI, and vector databases like Pinecone, ChromaDB, and Weaviate. You’ll also master deployment technology using Docker, serverless functions, and cloud platforms like AWS, GCP, and Azure. By the end of this course, you’ll be fully equipped to build, deploy, and manage autonomous AI agents that solve real scenarios with speed and accuracy.

Course Details:

  • Duration : 90 hours Sessions (12 Weeks)
  • Classroom Sessions: 7.5 Hours/Week
  • Student Effort (Self-Study): 1 - 2 Hours/Day

Pre-Requisites:

No prerequisites required – this Gen AI & Agentic AI Developer Virtual training is beginner-friendly and open to everyone

Outcome:

Students will gain practical skills in 

  • OpenAI
  • Anthropic
  • Gemini
  • LangChain
  • CrewAI
  • Autogen
  • API Development
  • Make.com
  • n8n
  • Zapier
  • Python
  • FastAPI
  • Cloud Deployment

Modules

Week 1: Introduction to Artificial Intelligence
• The Current AI Landscape
• Overview of AI, ML, Deep Learning, Neural Networks, NLP
• AI vs GEN AI vs Agentic AI

Week 2: Understanding the Basics
• Tokenization, Embeddings, Headings, Parameters.
• Vector DB, Relationships.
• Simulators

Week 3: Context and Prompt Engineering
• Context Engineering: Managing context in LLMs for agents
• Multi-Chain Prompting: Sequential reasoning, chaining
• Model Context Protocol (MCP): Context-aware agent design
• Prompt Strategies: Zero-shot, few-shot, Chain-of-Thought (CoT)
   Challenges: Context overflow, ambiguous prompts
• Solutions: Context truncation, iterative prompt refinement

Week 4: Advanced Patterns
• Meta Prompting
• Tree of Thoughts
•  ReAct (Reasoning + Acting)    • Perspective Prompting)            • System Prompting

Week 5: Agentic AI Fundamentals
  • Agentic AI Introduction: Definition, autonomy, applications in banking/retail
    – AI Agents vs. Agentic AI: Distinctions, capabilities
    – Comparison: Agentic AI, Generative AI, Traditional AI
    – Building Blocks: Perception, Action, Learning, Collaboration
    – Ethical AI: Bias mitigation, fairness, transparency
    – Real-Life Experiences: Case studies (e.g., banking automation success, chatbot failures in retail)
    – Challenges: Ethical dilemmas, scalability constraints
    – Solutions: Ethical frameworks, modular design
  • LLM Overview: GPT, Llama, T5 architectures, strengths
    – Capabilities: Text generation, classification, reasoning
    – Knowledge Graphs: Structure, integration with LLMs
    – Challenges: Hallucination, computational cost
    – Solutions: Knowledge grounding, efficient model selection
 
Week 6: Agentic AI Frameworks
  • LangChain: Components, chains, runnables, LCEL
    – LlamaIndex: Data indexing, retrieval for agents
    – LangGraph: State management, workflows
    – CrewAI: Introduction to collaborative agent systems
    – Autogen: Introduction to role-based agent interactions
    – Real-Life Challenges: Framework selection, integration complexity
    – Challenges: Compatibility issues, learning curves
    – Solutions: Modular design, debugging strategies
  • Vector Databases
    -Vector DBs: Pinecone, Weaviate, FAISS, use cases
    – Embeddings: Creating, storing, querying embeddings
    – Applications: Semantic search, recommendation systems
    – Challenges: Indexing latency, storage costs
    – Solutions: Optimized indexing, batch processing
Week 7 & 8: Data Processing with LangChain
  • LangChain Components:
      Document loaders, text splitters, embeddings
    – LCEL: Runnables, chains, deployment with Langserve
    – Real-Life Experiences: Pipeline bottlenecks, data quality issues
    – Challenges: Inconsistent data formats, processing delays
    – Solutions: Efficient chunking, robust error handling
  • Agentic RAG and Multimodal Retrieval
    – Agentic RAG vs. Traditional RAG: Adaptive retrieval mechanisms
    – VLM Integration: Vision-Language Models for multimodal tasks
    – SLM: Small Language Models for efficiency
    – LlamaIndex and Cohere: RAG frameworks, use cases
    – Knowledge Graphs in RAG: Enhancing retrieval accuracy
    – Challenges: Retrieval precision, multimodal alignment
    – Solutions: Hybrid search, fine-tuned embeddings

Week 8 & 9: Deep dive into advanced Generative AI concepts
– Fine-Tuning LLMs: Techniques (LoRA, quantization)
– Knowledge Graphs: Advanced integration with agents
– Evaluation Metrics: BLEU, ROUGE, perplexity, human evaluation
– Guardrails in GenAI: Bias mitigation, output validation
– Real-Life Experiences: GenAI deployment failures (e.g., hallucination in chatbots)
– Challenges: Inconsistent outputs, ethical risks
– Solutions: Robust evaluation, knowledge grounding

Week 10: Explore platforms for building Agentic AI systems, with emphasis on CrewAI and AutoGen
– Autogen: Role-based agents, conversation flows, A2I integration
– CrewAI: Collaborative agent orchestration, task delegation
– Phidata: Knowledge management, agent workflows
– CrewAI Features: Agent roles, task scheduling, collaboration
– Autogen Features: Multi-agent conversations, dynamic workflows
– Challenges: Platform compatibility, orchestration complexity
– Solutions: Modular design, reusable components

Week 11: Develop agents using no/low-code platforms
• Writing a Technical Resume & LinkedIn Optimization
• Mock Interviews (DSA, System Design, Project Discussion)
• Contributing to Open Source / Building a Portfolio on GitHub
Observability and Governance
– Observability Tools: Langfuse, Langsmith, AgentOps
– Tracing: Monitoring agent actions, decision paths
– Governance: Compliance, audit trails, regulatory adherence
– Guardrails: Preventing harmful outputs, prompt injection
– Evaluation Metrics: Accuracy, latency, user satisfaction
– Challenges: Data privacy, observability overhead
– Solutions: Lightweight tracing, anonymized logging

Week 12: Advanced Multi-Agent Applications & Cloud Environments
– Advanced Use Cases: Cross-domain collaboration (e.g., finance, healthcare)
– CrewAI Applications: Multi-agent workflows for complex tasks
– Autogen Applications: Conversational agents with dynamic reasoning
– Knowledge Integration: Combining LLMs with external tools
– Challenges: Scalability, cross-agent consistency
– Solutions: Workflow optimization, agent synchronization
Cloud Environments
– Cloud Platforms: AWS, Azure, GCP overview, services
– Azure OpenAI: Deploying LLMs, integration
– AWS SageMaker: Model training, hosting, deployment
– GCP Vertex AI: Agent Builder, model orchestration
– Serverless Deployment: Lambda, Azure Functions
– Challenges: Cost management, scalability issues
– Solutions: Autoscaling, cost monitoring tools

Week 13:
Course Wrap-Up
– Course Summary: Key takeaways, best practices for Agentic AI
– Real-Life Reflections: Lessons from industry deployments (e.g., banking, retail)
– Challenges: Deployment failures, client expectation gaps
– Solutions: Iterative testing, stakeholder alignment

Final Capstone Project
• Choose a full-stack project (e.g., Blogging Platform, Job Board, Task
Management App)
• Apply Agile methodologies (Trello, Jira)
• Weekly Code Review & Debugging Sessions

Week 14: Resume, Interview Preparation & Job Readiness
• Writing a Technical Resume & LinkedIn Optimization
• Contributing to Open Source / Building a Portfolio on GitHub

Why Learners Byte ?

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    Our courses are expertly crafted by IIT alumni and industry-experienced professionals
  • 26+ Years Of Experience
    We trained 1,00,000+ students with more than 25% Placements to our students
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    Leverage our extensive experience in corporate training with hands-on, impactful programs that drive employee success
  • Official Skills Enablement Partner of VTU
    Proud Partner of Visvesvaraya Technological University to deliver industry-aligned, future-ready skills
  • Comprehensive Course Offering
    A wide range of courses covering the latest in cloud computing, DevOps, AI/ML, data science, and full stack dev.
  • Strong Industry and Technology Partnerships
    Our robust connections with leading industries and technology partners enable students with certifications
  • Flexible Delivery Options
    Access to online, onsite, and hybrid training options with flexible payment options

GEN AI & AGENTIC AI Developer Course - Eligibility & Pre-requisites

Our GEN AI & AGENTIC AI Developer course is designed for graduates eager to start or advance their careers in web development. This beginner-friendly course requires no prior coding experience, making it accessible to learners from any field. Basic computer literacy and a keen interest in learning programming are the only prerequisites. Whether you’re a recent graduate or a professional looking to upskill, this course equips you with the tools and knowledge to excel as a GEN AI & Agentic AI Developer.

Recognized by AICTE

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Official Skills Enablement Partner of VTU

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Frequently Asked Questions about GEN AI & AGENTIC AI course

What will I learn in this Gen AI & Agentic AI Developer course?

You’ll learn the latest IT trends that gives you a global recognition..

This course is ideal for graduates, job seekers, and professionals with basic knowledge of Computer Sciene is eligible for this course.

Become job-ready for AI, automation, data, coding, EdTech, and freelancing roles with practical skills that open high-growth, future-proof opportunities.

Yes, the course includes real-world projects, such as building automations and agents across various Industries use cases and deploying applications.

For detailed fee structure and Payment options- Contact us at 👇

Details for a Free Consultation


Hear from our Alumni

Meena Sharma
This course helped me learn both front-end and back-end development, which is very useful for jobs today. The trainers explained every concept in a way that was easy to understand, even for someone from a non-technical background.
Sandeep
I always wanted to learn web development but didn’t know where to start. This course taught me everything from HTML and CSS to backend development in a simple and clear way. The best part was the hands-on projects, which made learning fun
Priyanka
This course is a perfect blend of front-end and back-end development. The way they structured the lessons, starting from HTML, CSS, and JavaScript to React, Node.js, and databases, made learning progressive and practical. The final project was the best part