Project 01
Python, Jupyter Notebook, Pandas, NumPy, Matplotlib, Seaborn, Plotly, SQL
Build ETL pipelines for simulation datasets Visualize agent-environment interactions Create an interactive data dashboard
Job-ready learning
Basic
Agentic AI.
Focus on autonomous AI agents, tool usage, multi-step workflows, reasoning, and orchestration.
Learner rating
4.8/5
Course duration
4 Months
Learning modules
7 modules
Learning mode
Online/ Offline
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Program overview
Agentic AI is a 4 Months Artificial Intelligence program built around hands-on practice, guided assignments, interview preparation, and placement support. Start with AI Agents, practise with OpenAI APIs, LangChain, CrewAI, and connect your learning through applied business scenarios. Build a portfolio that explains your approach and prepare for roles such as AI Agent Developer and AI Automation Engineer.
4 Months structured training program
Hands-on practice with OpenAI APIs, LangChain, CrewAI
Live projects and portfolio-ready assignments
Certification guidance and placement assistance
A structured learning journey
Agentic AI Curriculum
Explore the curriculum, build your understanding one module at a time, and put each new skill into practice.
01Advanced Python & Data EngineeringModule 1 · 2 topics
- Advanced Python, data pipelines, and simulation-ready datasets
- Advanced Python: OOP, decorators, generators, context managers, async programming Data Engineering Basics: ETL pipelines, handling large datasets Data Cleaning & Preprocessing: Missing data, outliers, feature scaling Data Visualization: Interactive dashboards with Plotly, Seaborn, Matplotlib
02Machine Learning & Feature EngineeringModule 2 · 2 topics
- Supervised learning, reinforcement learning basics, and feature engineering
- Supervised Learning: Regression, classification, ensemble methods Unsupervised Learning: Clustering, PCA Feature Engineering & Selection: Dimensionality reduction, embeddings Model Evaluation: Accuracy, F1-score, ROC-AUC Introduction to Reinforcement Learning (RL): Markov Decision Processes, reward functions
03Deep Learning & Neural NetworksModule 3 · 2 topics
- Advanced architectures for perception, sequence modeling, and decision-making
- ANN, CNN, RNN/LSTM for perception and sequence prediction Transformers for planning & reasoning tasks Attention Mechanism for multi-step decision-making Autoencoders for state representation learning
04Reinforcement Learning & Agentic AI FundamentalsModule 4 · 2 topics
- RL foundations, autonomous agents, and multi-step decision-making
- Deep Q-Networks (DQN), Policy Gradients, Actor-Critic methods Multi-Agent Systems & Cooperative/Competitive environments Hierarchical Reinforcement Learning Planning & Decision-Making Algorithms Agent Evaluation Metrics: Reward curves, convergence, safety
05Advanced Agentic AI & ToolsModule 5 · 2 topics
- Integrating LLMs with agents, multi-modal reasoning, and semantic memory
- Agentic LLMs: Planning, reasoning, tool-use with GPT/BERT LangChain Agents: Memory, tools, action planning Multi-modal Agentic AI: Text, image, and action-based decision-making Vector Databases & Knowledge Retrieval: FAISS, Pinecone AI Ethics & Safety: Bias, hallucination, responsible autonomous agents
06Deployment, MLOps & Capstone ProjectModule 6 · 2 topics
- Deploy agents, integrate AI into apps, portfolio-ready project
- Deployment: Streamlit, FastAPI, Docker, Kubernetes basics API Integration & Cloud Deployment: AWS/GCP/Azure MLOps: Versioning, monitoring, automated retraining Portfolio Development: Documentation, interactive dashboards Final Capstone: End-to-end autonomous agent application
07Career Opportunities After CompletionModule 7 · 1 topics
- Agentic AI Engineer: Build autonomous agents capable of multi-step reasoning and task execution Reinforcement Learning Engineer: Design and implement RL systems for games, simulations, and real-world tasks AI Research Engineer (Agentic AI): Explore new architectures for decision-making and multi-agent environments AI Solutions Architect: Design end-to-end autonomous AI solutions for products and enterprises Prompt & Agent Engineer: Integrate LLMs with autonomous agents for decision-making, planning, and tool use
Find out how this curriculum fits your career goals.
Tools & technologies
Explore industry-relevant tools through hands-on learning to master practical, in-demand skills.
LangChain
LangGraph
AutoGen
CrewAI
OpenAI API
Google Gemini API
Claude API
LlamaIndex
Hugging Face
n8n
Flowise
Python
Streamlit
FastAPI
Vector Databases (FAISS / ChromaDB)
Build practical skills with industry-relevant learning, expert guidance, and hands-on projects designed for modern teams.
Get the complete curriculum, tools, projects, and career support details in one brochure.
Build something that matters
6 Industry Projects
Every student completes 6 real-world projects — one per month — building a professional portfolio demonstrated during placement interviews.
Project 02
Industryscikit-learn, XGBoost, OpenAI Gym, Pandas, NumPy Scenario Project
Predict agent performance in simulation environments Cluster environment states for efficient decision-making Evaluate multiple RL algorithms on standard benchmarks
Project 03
TensorFlow, Keras, PyTorch, Hugging Face
Image classification for autonomous perception Sequence modeling for action prediction Anomaly detection in agent behavior using autoencoders
Project 04
OpenAI Gym, Stable Baselines, Ray RLlib, PyTorch
Train an agent to play a game environment (e.g., CartPole, LunarLander) Multi-agent cooperative navigation task Autonomous decision-making agent in a simulated environment
Project 05
LangChain, Hugging Face, FAISS, Pinecone, GPT, DALL·E
Build a planning agent using LangChain Multi-modal agent performing tasks using images and text inputs Knowledge retrieval agent using vector embeddings
Project 06
Streamlit, FastAPI, Docker, MLflow, GitHub, Cloud platforms, OpenAI API
Autonomous agent that performs multi-step tasks via a web interface AI planning assistant integrating text and action reasoning Multi-agent simulation with cooperative task completion
Portfolio tip: document the problem, your approach, and the result so you can walk an interviewer through your decisions.
Who should enroll
- Developers building AI workflows
- Automation professionals
- Data and backend engineers
- Students with basic programming knowledge
Learning modes
- Classroom training at Thane center
- Live online interactive sessions
- Recorded sessions for missed classes
- Free demo class before enrollment
Prerequisites
- Basic Python or JavaScript knowledge is helpful
- Understanding of APIs is helpful
- Own laptop for hands-on labs
95% of our students successfully get placed after completing our programs.
Learn more about how we have been impacting thousands of careers.
- Apply Agentic AI skills to practical business scenarios
- Use OpenAI APIs, LangChain, CrewAI with confidence
- Build project work that can be shown in interviews
- Prepare for relevant certifications and job roles
Recognition that travels with you
Globally recognized certifications
Agentic AI Certificate
Certification pathway
Stand out with proof of practical skills.
Build a credible portfolio of skills with structured preparation, practical projects, and certification guidance from experienced mentors.
Beyond the classroom
Career Assistance Program
Soft skill session
Resume building
Aptitude training
LinkedIn profile building
Mock interview
Job assistance
Placement Assistance
Dedicated placement support with industry connections and recruiter interactions.
Dedicated placement cell with job referrals
Resume & LinkedIn profile optimization
Mock interviews with industry experts
Interview call scheduling support
Career counseling until you get placed
Access your complete learning experience through our all-in-one learning portal.
SkillUpAll-in-one
01Live Mentor Sessions
02AI-Powered Practice
03Progress Tracking
04Career support
One learning hub for your complete career journey
Your next opportunity starts here
Limited seats. High demand. Enroll today.
Industry-ready courseFilling fastGet industry-ready with Wraystech Academy
- Enroll for one course and get access to the complete learning journey
- In-depth curriculum with live mentor support
- Integrated projects and industry-recognized certifications
- Practice on real-world cases and career-ready assignments
- Dedicated placement guidance and interview preparation
CAREER OUTCOME
Offer letter preview
Congratulations!
Your practical skills, projects, and career preparation can help you approach interviews with clarity and confidence.
Role-ready preparation
Portfolio, interview practice, and mentor feedback.
Career counsellor
READY
A little more clarity
Frequently asked questions
Answers to common questions about learning, projects, and getting started.
What is Agentic AI?
Agentic AI refers to artificial intelligence systems that can understand goals, make decisions, plan tasks, and take actions autonomously to complete complex workflows
What will I learn in the Agentic AI course?
You will learn AI agents, LLM fundamentals, prompt engineering, tool integration, agent workflows, multi-agent systems, memory management, and AI automation.
Who can join the Agentic AI course?
Students, freshers, software developers, IT professionals, AI enthusiasts, and working professionals interested in building intelligent AI applications can join this course.
Do I need programming knowledge to learn Agentic AI?
Basic Python programming knowledge is recommended because it helps you build, customize, and integrate AI agents.
What are the prerequisites for learning Agentic AI?
Basic knowledge of programming, Python fundamentals, and AI concepts is helpful. Beginners can start with the foundational modules before moving to advanced topics.
Which tools and technologies are covered in Agentic AI training?
The course may cover Python, LangChain, LangGraph, CrewAI, OpenAI API, Google Gemini API, LlamaIndex, n8n, ChromaDB, and Streamlit.
What is the difference between Generative AI and Agentic AI?
Generative AI primarily creates content such as text, images, and code based on user prompts. Agentic AI goes further by planning tasks, using tools, making decisions, and executing multi-step workflows.
What are AI agents?
AI agents are software systems powered by AI models that can understand instructions, plan actions, use external tools, and perform tasks to achieve specific goals.
What is a Multi-Agent AI System?
A Multi-Agent AI System consists of multiple AI agents that collaborate, share tasks, and coordinate their actions to solve complex problems.
Does the Agentic AI course include practical training?
Yes, students gain hands-on experience by developing AI agents, integrating APIs, creating automated workflows, and building real-world AI applications.
What projects will I build during the Agentic AI course?
Students can work on projects such as AI-Powered Personal Assistant, Multi-Agent Research Assistant, AI Customer Support Agent, and AI Workflow Automation System.
What is LangChain used for in Agentic AI?
LangChain is a framework used to build applications powered by large language models. It helps connect AI models with tools, data sources, and external services.
What career opportunities are available after completing Agentic AI training?
You can explore roles such as AI Engineer, AI Agent Developer, Generative AI Developer, AI Automation Engineer, and Python AI Developer, depending on your skills and experience.
Is Agentic AI in demand in the IT industry?
Agentic AI is an emerging area of artificial intelligence. Organizations are exploring AI agents for workflow automation, research, customer support, and business process improvement.
Does Raystech Academy provide placement assistance after Agentic AI training?
Raystech Academy provides career guidance, resume-building support, interview preparation, and placement assistance to help students prepare for relevant job opportunities.
Your next chapter starts with a conversation.
Discuss your background, explore the course, and get help choosing a learning plan that fits your goals.
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