Live training · Online & classroom · Hyderabad
Agentic AI Course in Hyderabad
Learn to build AI agents, LLM applications, RAG systems, tool-calling workflows and multi-agent systems through hands-on projects and practical, instructor-led training.
✓ Live instructor-led training ✓ Hands-on AI agent projects
✓ Real-world use cases ✓ Career & interview support
✓ Online + classroom options ✓ 3-month program
Free demo class
Book Your Free Demo
Next batch: 20 Jan 2026 · Online & classroom
Or WhatsApp us at +91 79959 25858
✓ Live instructor-led training • Hands-on projects • Real-world use cases • Online + classroom • Internship support • Career guidance • Interview preparation • Weekday & weekend batches • Google rating 5.0 ★ • 200+ AI & ML learners • Next batch: 20 Jan 2026
At a glance
Agentic AI Course Snapshot
Duration
3 Months
Modes
Online · Classroom · Video
Next batch
20 Jan 2026 · 1:00 PM
Fee
₹15,000 – ₹20,000
Certification
Certificate in Agentic AI
Level
Beginner to Advanced
Trainer experience
10+ Years
Location
Near JNTU Metro, Hyderabad
Agentic AI Course in Hyderabad: Quick Facts
Course
Agentic AI Course (AI Agents, LLMs, RAG, LangChain, LangGraph, Multi-Agent Systems)
Trainers
Mr. Sandeep, Ms. Shaheda Tabassum (10+ years experience)
Next batch
20 January 2026, 01:00 PM IST
Training modes
Online and offline (classroom) training, plus video course
Duration
3 months (online and offline)
Course fee
₹15,000 – ₹20,000 (depends on batch and mode)
Prerequisites
Basic programming knowledge; Python helpful but not mandatory
Internship & placement
Internship until you are placed, resume and LinkedIn support, mock interviews
Call / WhatsApp / Email
Demo class
Your learning path, start to job-ready
1 Python → 2 LLM Fundamentals → 3 Prompt Engineering → 4 AI Agents → 5 Tool Calling → 6 RAG → 7 Memory → 8 LangChain → 9 LangGraph → 10 Multi-Agent Systems → 11 MCP → 12 AI Automation → 13 Evaluation & Guardrails → 14 Deployment → 15 Real Projects → 16 Career
The basics
What Is Agentic AI?
Agentic AI is a way of building AI systems that work towards a goal on their own: they plan the steps, use tools and data, remember context, take actions and check the result, with human oversight where it matters.
Traditional AI models make a single prediction, and a basic chatbot answers a single prompt. An AI agent runs a loop instead. It uses a large language model (LLM) to decide what to do next, calls a tool or looks something up, observes what came back, and keeps going until the task is done.
That's why Agentic AI is about engineering as much as models: prompts, tools, retrieval, memory, workflows, guardrails and evaluation all have to work together.
How agents use LLMs
The LLM is the reasoning engine: it interprets the goal, chooses the next step and writes the final answer.
How agents plan
They break a goal into steps, run them, and re-plan when a step fails or returns something unexpected.
How agents use tools
Through tool (function) calling, the model asks your code to search, query a database or call an API.
How memory works
Short-term memory keeps the current task's context; long-term memory stores facts and history across sessions.
How agents collaborate
In multi-agent systems, specialised agents (planner, researcher, reviewer) split the work and hand off results.
How agents reach real systems
APIs, databases and business apps let agents act: update a CRM, file a ticket, send a report.
Why now
Why Learn Agentic AI in 2026?
Software is moving from programs that follow fixed rules to systems that can reason about a goal and act on it. Each step below builds on the one before, and the skills compound.
01 · Traditional software
Explicit rules, written by hand for every case.
02 · AI applications
Models learn patterns to predict and classify.
03 · Generative AI
LLMs generate text, code and images on request.
04 · AI agents
LLMs plan, use tools and act on a goal.
05 · Multi-agent systems
Specialised agents coordinate complex work.
Where these skills are used
Companies are exploring agents for business process automation, AI copilots for employees, decision support, and intelligent workflows that connect many tools. Building them reliably needs AI engineering skills — not just prompting.
AI automation • LLM applications • Intelligent workflows • Tool-using agents • Business process automation • AI copilots • Multi-agent collaboration • Enterprise AI • Decision support • AI engineering
What makes this program different
Why Choose Agentic AI Masters?
A course built around shipping working agents, not watching slides. Every module ends with something you've built and can explain in an interview.
Practical AI agent development
Build agents from a plain loop first, so you understand what frameworks do for you.
Hands-on LLM applications
Prompts, structured outputs and model selection, practised on real tasks.
RAG implementation
Embeddings, vector databases and retrieval pipelines over your own documents.
Tool calling & API integration
Give agents search, database and API tools so they can take real actions.
LangChain
Connect models to tools, retrievers and memory with a standard framework.
LangGraph
Stateful agent workflows with branching, loops and human-in-the-loop steps.
Multi-agent systems
Role-based teams and supervisor patterns with CrewAI, AutoGen and LangGraph.
AI automation workflows
Automate multi-step business processes across connected systems.
Agent memory
Short- and long-term memory and context management that keeps agents on track.
Evaluation & guardrails
Test agents, defend against prompt injection, and monitor behaviour.
Real-world projects
Support agents, RAG assistants, research agents and an end-to-end capstone.
Career & interview support
Resume and LinkedIn support, mock interviews and career guidance.
Attend a free demo class
Meet the trainer, see the syllabus and a live agent build. No obligation.
Is this for you?
Who Should Join This Agentic AI Course?
Built for anyone who wants to build AI agents, whether you are starting out or already work in tech.
Beginners
New to AI and want a structured path from basics to building agents.
Students & freshers
Graduates preparing for AI-related roles with a project portfolio.
Software developers
Developers who want to build LLM-powered apps and AI agents.
Python developers
Move into AI engineering and automation with skills you already have.
Data scientists
Add Agentic AI and LLM application development to your toolkit.
ML engineers
Extend from models to autonomous, tool-using AI systems.
Working professionals
Upskill on weekday or weekend batches without leaving your job.
Automation professionals
Upgrade RPA and workflow skills with AI-powered automation.
Entrepreneurs
Understand and build AI automation for your own business.
AI enthusiasts
Get hands-on with the technology shaping autonomous AI systems.
Prerequisites
Basic programming knowledge is recommended. Familiarity with Python, machine learning and APIs helps you learn faster, but you don't need to master advanced concepts before starting.
01 · Basic programming knowledge
Familiarity with programming concepts; Python is helpful for AI development.
02 · AI & ML basics
A general understanding helps, but advanced expertise is not required.
03 · Logical & analytical thinking
Interest in solving problems and understanding how AI systems make decisions.
04 · Familiarity with APIs
Basic awareness of how software applications connect is useful.
05 · Willingness to learn
Curiosity and commitment to practice matter most.
06 · Basic technical comfort
Comfort installing tools and following technical instructions.
3 months · project in every module
Agentic AI Course Curriculum
From Python and LLM fundamentals to deployed multi-agent systems. Every module lists its topics, tools, a practical exercise and what you'll be able to do.
Module 01: Introduction to Agentic AI
Topics
• What is Agentic AI?
• AI vs Generative AI vs Agentic AI
• How AI agents work
• Agent components
• Autonomous workflows and real-world applications
Tools: Concepts and architecture diagrams
Practical exercise: Break a real business task into a goal, steps, tools and checks.
Project application: Design brief for your capstone agent.
You'll be able to: Explain clearly how an agent differs from a chatbot.
Module 02: Python & AI Foundations
Topics
• Python essentials: functions, data structures
• Working with APIs
• JSON
• Async programming
• AI/ML libraries
Tools: Python, REST APIs, JSON
Practical exercise: Call a public API asynchronously and parse the JSON response.
Project application: Utility layer reused by every later agent.
You'll be able to: Write the Python glue code agents depend on.
Module 03: LLM Fundamentals & Prompt Engineering
Topics
• LLM fundamentals and architecture
• Tokens and context windows
• System and user prompts, few-shot prompting
• Structured outputs and validation
• Function / tool calling basics
• Model selection
Tools: OpenAI API
Practical exercise: Get validated, structured JSON from a model every time.
Project application: Structured-output document extractor.
You'll be able to: Control LLM output reliably and choose the right model.
Module 04: AI Agent Architecture
Topics
• Perception, reasoning and planning
• Memory and tool use
• Agent loops
• State management
• Agent workflows
Tools: Python, OpenAI API
Practical exercise: Build a plan → act → observe loop from scratch.
Project application: Minimal agent with no framework.
You'll be able to: Understand exactly what agent frameworks automate.
Module 05: Tools, APIs & Automation
Topics
• Tool calling
• API integration
• Web and search tools
• Database tools
• Workflow automation
• External system integration
Tools: Function calling, REST APIs, databases
Practical exercise: Give an agent search and database tools.
Project application: Task-automation agent connected to real services.
You'll be able to: Connect agents safely to external systems.
Module 06: RAG & Agent Memory
Topics
• Embeddings
• Vector databases
• Chunking and retrieval
• RAG pipelines
• Short-term and long-term memory
• Knowledge-based agents
Tools: Embeddings, vector database
Practical exercise: Index a document set and answer with sources.
Project application: RAG knowledge assistant.
You'll be able to: Ground answers in your own data.
Module 07: Agent Frameworks: LangChain & LangGraph
Topics
• LangChain: models, tools, retrievers, memory
• LangGraph: nodes, edges and state
• Agent workflows
• Tool integration
• State management and human-in-the-loop
Tools: LangChain, LangGraph
Practical exercise: Rebuild your agent as a LangGraph state graph.
Project application: Stateful customer support agent.
You'll be able to: Build maintainable, debuggable agent workflows.
Module 08: Multi-Agent Systems
Topics
• Multi-agent architecture
• Agent communication
• Role-based agents
• Collaboration and task delegation
• Supervisor patterns
• Multi-agent workflows
Tools: CrewAI, AutoGen, LangGraph
Practical exercise: Build a planner + specialist + reviewer team.
Project application: Multi-agent research workflow.
You'll be able to: Decide when multiple agents help, and when they don't.
Module 09: Agent Security, Guardrails & Evaluation
Topics
• AI safety and human oversight
• Guardrails
• Prompt injection
• Data protection
• Agent evaluation
• Monitoring
Tools: Evaluation and guardrail techniques
Practical exercise: Attack your own agent with prompt injection, then defend it.
Project application: Evaluation suite for your capstone.
You'll be able to: Measure and harden agent behaviour before release.
Module 10: Deployment & Real-World Projects
Topics
• Agent deployment
• Cloud basics
• Production considerations
• Monitoring
• End-to-end projects and case studies
Tools: API deployment, cloud basics
Practical exercise: Deploy an agent behind an API endpoint.
Project application: End-to-end capstone agent.
You'll be able to: Ship a working agent that other people can use.
Module 11: Career Preparation
Topics
• Project portfolio
• GitHub
• Resume
• LinkedIn
• Interview preparation
• Explaining your AI projects
Tools: GitHub, LinkedIn
Practical exercise: Mock interview: walk through your capstone architecture.
Project application: Portfolio of documented agent projects.
You'll be able to: Present your work confidently to employers.
Module 12: MCP & AI Tool Connectivity
Topics
• Model Context Protocol concepts
• MCP clients and servers
• Tools and resources
• Connecting models to external systems
Tools: MCP
Practical exercise: Expose a tool through an MCP server.
Project application: MCP-based assistant.
You'll be able to: Connect agents to tools through a standard protocol.
Stack
Tools & Technologies
The frameworks and services you'll use in class: what each one does and where you'll use it.
Python
The main language for AI and agent development.
Used in → Every module, from API calls to deployment.
OpenAI API
Access to LLMs for reasoning, generation and tool calling.
Used in → Prompting, structured outputs, agent loops.
LangChain
Standard building blocks for models, tools, retrievers and memory.
Used in → Tool-using and retrieval-based agents.
LangGraph
Stateful, graph-based agent workflows with control flow.
Used in → Support agent, human-in-the-loop flows.
CrewAI
Role-based teams of agents that coordinate tasks.
Used in → Multi-agent business workflows.
AutoGen
Conversational multi-agent systems that collaborate.
Used in → Research and reviewer-agent patterns.
Embeddings & vector databases
Store and search meaning, not just keywords.
Used in → RAG pipelines and knowledge agents.
APIs & function calling
Let agents call real services with structured arguments.
Used in → Automation, CRM and data agents.
MCP
Model Context Protocol: a standard way to connect models to tools and data.
Used in → MCP-based assistant project.
TensorFlow & PyTorch
Frameworks for building and training neural-network models.
Used in → AI & ML foundations.
Stable-Baselines3 & RLlib
Libraries for reinforcement learning and agent training.
Used in → Exploring how agents learn from feedback.
Git & GitHub
Version control and a public home for your project code.
Used in → Portfolio and capstone.
Outcomes
Skills You Will Master
By the end of the course you'll be able to design, build, test and deploy AI agents end to end.
1. Autonomous AI agent development
Design agents that reason, plan, use tools and execute tasks with minimal intervention.
2. LLM integration & tool calling
Build apps with LLMs, APIs, function calling and external tools.
3. Prompt engineering
System prompts, few-shot prompting and validated structured outputs.
4. RAG pipelines
Embeddings, vector databases and retrieval over your own documents.
5. Agent memory & context
Manage conversation history and long-term memory across tasks.
6. LangChain & LangGraph
Build maintainable, stateful agent workflows with leading frameworks.
7. Multi-agent system design
Make agents collaborate, communicate and coordinate complex work.
8. AI-powered automation
Automate support, data processing, reporting and business operations.
9. MCP tool connectivity
Connect agents to tools and data through the Model Context Protocol.
10. Guardrails & security
Defend against prompt injection and protect sensitive data.
11. Agent testing & deployment
Evaluate performance, apply guardrails and deploy reliable agents.
12. Problem-solving with AI
Apply AI to real business and technical challenges.
Portfolio
Real-World AI Agent Projects
Each project solves a business problem with a real agent architecture. You'll finish with working code on GitHub and a clear story for interviews.
01 · AI Customer Support Agent
Ticket → Agent → Knowledge search → Draft → Human approval
Problem: Support teams answer the same questions repeatedly.
Stack: LangGraph, RAG, helpdesk API
Skills: Tool calling, RAG, human-in-the-loop
Outcome: Drafted, sourced replies for review.
02 · RAG Knowledge Assistant
Documents → Chunks → Embeddings → Retrieval → LLM → Answer
Problem: Answers are buried in long documents.
Stack: Embeddings, vector DB, LangChain
Skills: Chunking, retrieval, citations
Outcome: Grounded answers with sources.
03 · AI Research Agent
Search → Analyse → Summarise → Report
Problem: Research takes hours of searching and reading.
Stack: Search tools, LLM, Python
Skills: Planning, tool use, synthesis
Outcome: A structured research brief.
04 · Multi-Agent Business Workflow
Planner → Specialist agents → Reviewer → Final result
Problem: Complex tasks need several kinds of expertise.
Stack: CrewAI / AutoGen / LangGraph
Skills: Roles, delegation, supervision
Outcome: Reviewed output from a coordinated team.
05 · AI Resume & Career Agent
Profile → Analysis → Recommendations → Output
Problem: Candidates struggle to tailor profiles to roles.
Stack: LLM, structured outputs
Skills: Prompt design, structured data
Outcome: Role-specific resume suggestions.
06 · AI Data Analysis Agent
Question → Agent → Python / SQL tools → Analysis → Insights
Problem: Business users can't query data themselves.
Stack: Python, database tools
Skills: Code-executing tools, validation
Outcome: Plain-language insights from data.
07 · AI Sales Assistant
Lead → Qualification → CRM / API → Follow-up draft
Problem: Leads go cold without timely follow-up.
Stack: APIs, function calling
Skills: API integration, workflow logic
Outcome: Qualified leads with drafted follow-ups.
08 · Workflow Automation Agent
Business task → Agent → Tools → Workflow → Result
Problem: Repetitive multi-app tasks waste staff time.
Stack: APIs, LangGraph
Skills: Orchestration, error handling
Outcome: An automated, monitored workflow.
09 · Enterprise Knowledge Agent
Company data → RAG → Agent → Guardrails → Secure response
Problem: Staff need secure answers from company data.
Stack: RAG, guardrails
Skills: Access control, data protection
Outcome: Permission-aware answers.
10 · MCP-Based AI Assistant
LLM → MCP client → MCP servers → External systems
Problem: Every tool integration is custom-built.
Stack: MCP
Skills: Protocol-based tool connectivity
Outcome: One assistant, many standard connectors.
Certification
Agentic AI Certification
On successful completion of the course you receive a Certification in Agentic AI from Agentic AI Masters, validating your skills in autonomous AI agents, multi-agent systems and AI-driven automation.
✓ Industry recognition — shows hands-on skill in advanced AI techniques used across cloud, cybersecurity, business automation and finance.
✓ Career advancement — a strong addition to your resume and LinkedIn for roles such as AI engineer, AI agent developer and automation engineer.
✓ Backed by projects — pair the certificate with your GitHub portfolio of agent projects.
Where this leads
Agentic AI Career Opportunities
Roles you can work towards with these skills. The right fit depends on your background, your portfolio and your interviews.
AI Agent Developer
Designs and builds agents that use LLMs, tools, memory and workflows.
Skills: Python, LangGraph, tool calling, RAG
Growth → Senior agent developer → AI architect
AI Engineer
Builds and integrates AI features into products and internal systems.
Skills: Python, APIs, LLMs, deployment
Growth → Senior AI engineer → tech lead
Generative AI Engineer
Builds applications on generative models: prompting, RAG, fine-tuning choices.
Skills: LLMs, RAG, evaluation
Growth → Lead GenAI engineer
AI Automation Engineer
Automates business processes with agents connected to company tools.
Skills: APIs, workflows, orchestration
Growth → Automation lead → solutions architect
LLM Application Developer
Develops chat, search and assistant apps powered by LLMs.
Skills: Prompting, structured outputs, RAG
Growth → Senior developer → AI engineer
AI Solutions Engineer
Adapts AI solutions to client needs and supports implementation.
Skills: Agents, integration, communication
Growth → Solutions architect
Machine Learning Engineer
Builds and deploys ML models and the systems around them.
Skills: Python, ML, MLOps, LLM apps
Growth → Senior MLE → ML lead
AI Solutions Architect
Plans end-to-end AI systems that connect agents with business platforms.
Skills: Architecture, cloud, security
Growth → Principal architect (usually needs prior experience)
AI Consultant
Advises teams on where agents fit and how to adopt them responsibly.
Skills: Use-case analysis, AI literacy
Growth → Practice lead
AI Application Developer
Builds full applications with AI features end to end.
Skills: Python, APIs, front-end basics
Growth → Full-stack AI engineer
AI Product Engineer
Ships AI-powered product features with a focus on user value.
Skills: Prototyping, evaluation, product sense
Growth → Product-focused AI lead
Salary & career insights
In Hyderabad, professionals specialising in Agentic AI and related fields can expect competitive salaries. Typical ranges are below (per annum); your own offer depends on experience, technical skills, portfolio, location, company, interview performance, previous role and technology depth.
Role
Fresher
Senior level
AI Agent Developer
₹4.5 – 7 LPA
₹12 – 18 LPA
AI System Architect
₹6 – 8 LPA
₹15 – 25 LPA
AI Agent Training Specialist
₹4 – 6 LPA
₹12 – 18 LPA
AI Research Scientist
₹5 – 7 LPA
₹14 – 20 LPA
AI Automation Engineer
₹4.5 – 6.5 LPA
₹12 – 18 LPA
AI Solution Architect
₹6 – 8 LPA
₹15 – 22 LPA
Autonomous Systems Engineer
₹5 – 7 LPA
₹13 – 20 LPA
AI Consultant
₹5 – 6.5 LPA
₹14 – 20 LPA
AI Developer (Freelance)
₹6 – 8 LPA
₹12 – 18 LPA
AI Integration Specialist
₹5 – 7 LPA
₹12 – 18 LPA
Step by step
Agentic AI Career Roadmap
Five stages from your first Python script to interview-ready. Each stage builds on the last.
01 · Foundations
↓ Python
↓ AI & LLM fundamentals
↓ Prompt engineering
02 · Build agents
↓ AI agents
↓ Tool calling
↓ RAG
↓ Memory
03 · Orchestrate
↓ LangChain
↓ LangGraph
↓ Multi-agent systems
↓ MCP
↓ AI automation
04 · Ship
↓ Evaluation & guardrails
↓ Deployment
↓ Real projects
05 · Career
↓ Portfolio
↓ Interview preparation
↓ AI career
Who teaches you
Trainers
Instructors with 10+ years of experience, focused on practical AI agent development: short concept explanations, guided implementation, then real project exercises.
Mr. Sandeep
AI Expert & Lead Instructor
Focuses on helping students understand how AI agents, LLMs and automation workflows work together, with an emphasis on hands-on development and project-based learning.
Areas of instruction: LLM-based AI agents · Multi-agent systems · RAG & memory · AI workflow automation · Tool & API integration
Teaching approach: practical concepts, guided implementation and real-world project exercises.
ST
Ms. Shaheda Tabassum
AI Expert & Lead Instructor
She enjoys teaching and is committed to preparing the next generation of data scientists. Ms. Tabassum emphasises a practical approach, so students learn through real projects rather than only theory. Many of her students have earned significant certifications and secured excellent jobs in the field.
Areas of instruction: Data science · Machine learning · AI agents & LLMs · Real-world projects
Teaching approach: project-first, practical learning.
Learn your way
Learning Modes
Same curriculum and projects, whichever mode you choose.
Classroom training
Instructor-led sessions at our centre near JNTU Metro Station, Hyderabad.
• Live project included
• Doubt clarification in session
• Course completion certificate
Live online training
Interactive, trainer-led classes from anywhere.
• Live project-based learning
• WhatsApp learning support
• Career guidance & interview prep
Video / self-paced
Recorded lessons with course materials, at your own pace.
• Practical assignments
• WhatsApp support
• Course completion certificate
Weekday batches
Regular weekday sessions for students, freshers and those with flexible schedules.
Weekend batches
Upskill without leaving your job: built for working professionals.
Schedule
Upcoming Agentic AI Batches
Seats and timings are confirmed by a counsellor. Book a demo to reserve a place.
Mode
Days
Time (IST)
Start date
Duration
Availability
Classroom (offline)
Weekdays
01:00 PM
20 January 2026
3 months
Enrolling
Live online
Weekdays
01:00 PM
20 January 2026
3 months
Enrolling
Weekend
Sat & Sun
On request
Next available batch
3 months
Enrolling
Course fees: ₹15,000 – ₹20,000
Depends on the batch (online or offline) and additional support services. Ask us about payment options.
How it works
Your Journey: From Demo to Job-Ready
Step 1 · Day 1 — Free demo & counselling
See the syllabus, meet the trainer and pick your mode and batch.
Step 2 · Week 1 — Enrol & set up
Get your environment, tools and course materials ready.
Step 3 · Months 1–3 — Learn & build
Live classes, assignments and an agent project in every module.
Step 4 · Month 3 — Capstone & internship
Build and deploy an end-to-end agent; start the internship track.
Step 5 · After course — Placement assistance
Resume, LinkedIn, mock interviews and job referrals until placed.
Before You Pay: A 6-Point Checklist
Whichever institute you choose, check these first. We're happy for you to check every one with us.
✓ Attend a free demo
Judge the teaching style before you pay anything.
✓ Meet the actual trainer
Know who will teach you and their experience.
✓ Read the full syllabus
Check it covers agents, RAG, LangGraph and multi-agent systems.
✓ Ask to see sample projects
Real agent projects matter more than slides.
✓ Confirm batch timing & mode
Make sure the schedule fits your college or job.
✓ Understand fees & support
Get the fee, payment options and career support in writing.
Learner voices
Student Success Stories
What learners say about building agents with us.
★★★★★
“The Agentic AI certification course was a game-changer for me. I learned how to build autonomous AI agents from scratch, and the hands-on projects gave me the confidence to implement AI solutions in my workplace. The instructors were highly knowledgeable.”
Ramesh K · AI Developer
★★★★★
“I've always worked with data, but this course taught me how AI agents can autonomously solve problems and make decisions. The learning experience was rich and practical, with lots of opportunities to work on live projects.”
Priya M · Data Scientist
★★★★★
“As someone already working in cybersecurity, this course was exactly what I needed. Learning how to create autonomous security agents and integrate them with cloud platforms was a game-changer.”
Karthik J · Cybersecurity Expert
★★★★★
“The course helped me bridge the gap between cloud computing and AI. I learned how to build intelligent cloud automation solutions using AI agents. The trainers were great and the materials were up to date.”
Ananya S · Cloud Engineer
5.0 ★ Google rating • 200+ AI & ML learners • 90% practical, project-based learning • 5+ hands-on assignments & use cases
Companies That Hired From Agentic AI Masters
…and Cognizant.
After the classes
Internship & Placement Support
Here's exactly what career support includes, so you know what to expect.
Internship until you are placed, with full placement assistance: resume building, mock interviews and job referrals.
Final hiring decisions are made by employers and depend on your skills, projects and interview performance.
Resume support
Rewrite around the agents you built.
LinkedIn optimisation
Profile, headline and project posts.
Mock interviews
Technical and project walkthroughs.
Project portfolio
Documented repos on GitHub.
Interview preparation
Common Agentic AI questions.
Job guidance
Which roles fit your profile.
Internship support
Practical experience on projects.
Career counselling
Before, during and after the course.
Compare before you choose
How Agentic AI Masters Is Different
Many AI courses stop at generative AI basics. Here's what a dedicated Agentic AI program adds.
What to compare
Agentic AI Masters
Typical generic AI course
Curriculum focus
✓ End-to-end Agentic AI: agents, RAG, memory, multi-agent, deployment
Mostly generative AI or general ML theory
Frameworks
✓ LangChain, LangGraph, CrewAI, AutoGen, MCP, OpenAI API
One framework or none
Projects
✓ 10 real-world agent projects plus a deployed capstone
Small demos or notebook exercises
Trainers
✓ Named trainers with 10+ years of experience
Trainer often not disclosed upfront
Learning modes
✓ Classroom, live online and video course
Usually only one mode
Batches
✓ Weekday and weekend batches
Fixed schedule
Career support
✓ Internship until placed, resume, LinkedIn, mock interviews, referrals
Certificate only
Try before you pay
✓ Free demo class with the trainer
Pay first
Compare
Agentic AI vs Generative AI
Agentic AI builds on generative AI. Generative models create content; agentic systems wrap those models in planning, tools and memory so they can complete multi-step tasks.
Feature
Generative AI
Agentic AI
Generates content
Yes, its core purpose
Yes, as one step in a larger task
Uses tools
Sometimes
Core capability
Planning
Limited, within one response
Plans and re-plans across steps
Memory
Depends on the application
Often integrated (short- and long-term)
Multi-step execution
Limited
Designed for it
Automation
Possible
A major use case
Multi-agent collaboration
Not required
A common architecture
In practice
Agentic AI Use Cases
What agents can realistically do today, with people reviewing the decisions that matter.
Business automation
Run multi-step back-office tasks across connected apps, escalating exceptions to people.
Customer support
Draft sourced replies, triage tickets and look up order or account details.
Software development
Assist with code review, test generation and documentation under developer supervision.
Research
Search, read and summarise sources into structured briefs.
Finance
Support data analysis, research automation and fraud-monitoring workflows.
Healthcare
Help with administrative tasks and information retrieval, with clinicians in control.
Cybersecurity
Assist with alert investigation and threat analysis while analysts make the decisions.
Cloud operations
Monitor resources, summarise incidents and suggest remediation steps.
Sales
Qualify leads, update CRM records and prepare follow-up drafts.
Marketing
Research audiences, draft campaign variants and compile performance reports.
Data analysis
Answer business questions by writing and running queries, then explaining results.
Enterprise knowledge
Give staff permission-aware answers from internal documents.
Local
Why Learn Agentic AI in Hyderabad?
Hyderabad is one of India's largest technology hubs, home to global IT services firms, product companies, captive technology centres and a growing startup ecosystem. As these teams adopt LLMs and automation, practical AI engineering skills are increasingly relevant for the city's large base of software professionals.
Learning locally means you can attend classroom sessions, meet trainers in person and work through projects alongside peers, with live online classes as an alternative when you can't travel.
CLASSROOM CENTRE — Agentic AI Masters
3rd floor, Dr. Atmaram Estates, near JNTU Metro Station, next to Bhramaramba Theatre, Vasanth Nagar, Hyder Nagar, Hyderabad, Telangana 500072
Mon–Sat · 9 am – 9 pm · a short walk from JNTU College metro station
Answers
Frequently Asked Questions
Short, direct answers. Still unsure? Ask us on WhatsApp.
What is Agentic AI?
Agentic AI is an approach to building AI systems that work towards a goal by planning steps, using tools, keeping memory and taking actions, rather than only answering a single prompt. An AI agent uses a large language model to decide what to do next, calls tools or APIs, checks the result and repeats until the task is done, with human oversight where needed.
What is an Agentic AI course?
An Agentic AI course teaches you to design, build, test and deploy AI agents. At Agentic AI Masters it covers Python foundations, LLMs and prompt engineering, agent architecture, tool calling, RAG and memory, LangChain and LangGraph, multi-agent systems, guardrails, evaluation and deployment, with hands-on projects in every stage.
What is Agentic AI training in Hyderabad like at Agentic AI Masters?
Training is live and instructor-led, in our classroom near JNTU Metro Station in Hyderabad or online. Sessions combine short concept explanations with guided implementation, so you build working agents in class and extend them through assignments and projects.
Who can learn Agentic AI?
Software and Python developers, data scientists, ML engineers, freshers and graduates, working professionals, automation specialists and entrepreneurs can all learn Agentic AI. Developers progress fastest because agents are built in code; beginners start with the Python and LLM foundations modules.
Can freshers learn Agentic AI?
Yes. Freshers begin with Python and AI foundations before moving on to agents. Basic programming familiarity helps, and steady practice matters more than prior AI experience. The projects you build become portfolio pieces you can discuss in interviews.
Do I need to know Python?
Basic programming knowledge is recommended, and Python helps because most agent frameworks are Python-first. You don't need to be an expert: the course includes a Python & AI foundations module covering essentials, APIs, JSON and async programming.
Do I need AI or machine learning experience?
No. A general understanding of AI or data concepts helps, but advanced expertise isn't required. Agent development relies mostly on software skills such as APIs, data handling and workflow design, combined with knowing how LLMs behave, which the course teaches from the basics.
What is the difference between Generative AI and Agentic AI?
Generative AI produces content such as text, images or code in response to a prompt. Agentic AI uses generative models as the reasoning engine inside a system that plans multi-step work, calls tools and APIs, keeps memory and acts on external systems. Most agents are built on generative models; the difference is autonomy and action.
What is an AI agent?
An AI agent is software that uses an LLM to decide what to do next towards a goal. It runs a loop: understand the request, plan, call a tool or retrieve knowledge, observe the result, and continue until done. An agent combines a model, instructions, tools and memory.
What is RAG?
RAG (Retrieval-Augmented Generation) lets an LLM answer using your own documents. Content is split into chunks, converted to embeddings and stored in a vector database. At question time the most relevant chunks are retrieved and given to the model, so answers are grounded in your data rather than the model's memory alone.
What is LangChain?
LangChain is an open-source framework for building LLM applications. It provides standard components for models, prompts, tools, retrievers and memory, making it faster to connect an LLM to data sources and APIs. In the course you use it to build tool-using and retrieval-based agents.
What is LangGraph?
LangGraph is a framework from the LangChain team for building stateful agent workflows as graphs. Nodes are steps such as model or tool calls, edges define the control flow, and shared state persists between steps. It supports loops, branching and human-in-the-loop approval, which makes complex agents easier to control and debug.
What are multi-agent systems?
A multi-agent system splits a task across several specialised agents, for example a planner, researcher, writer and reviewer, that communicate and hand off work, often coordinated by a supervisor agent. The course covers role-based agents, supervisor patterns and collaboration with CrewAI, AutoGen and LangGraph.
What is tool calling?
Tool calling (also called function calling) lets an LLM ask your code to run a specific function, such as searching the web, querying a database or calling an API, with structured arguments. Your application runs it and returns the result to the model. It is how agents take actions beyond generating text.
What is MCP?
MCP (Model Context Protocol) is an open standard for connecting AI applications to external tools and data through a common client–server interface, so one integration can work across many AI apps and agents. MCP is covered in Module 12 of the course.
What projects will I build?
Projects include AI customer support agents, RAG knowledge assistants, research agents and multi-agent workflows, finishing with an end-to-end capstone that you deploy. Each project uses the tools from its module, so by the end you have several working agents for your portfolio.
Is online training available?
Yes. Live online classes are interactive, trainer-led sessions with project-based learning, WhatsApp learning support, and career guidance and interview preparation. Ask a counsellor whether session recordings are provided for your batch.
Is classroom training available?
Yes. Instructor-led classroom training runs at our Hyderabad centre: 3rd floor, Dr. Atmaram Estates, near JNTU Metro Station, Hyder Nagar, Hyderabad 500072. Classroom learners get in-session doubt clarification, a live project and career guidance.
What is the course duration?
The course runs for 3 months in both online and classroom modes. Class days and timings vary by batch; see upcoming batches above or ask a counsellor for the current schedule.
What is the current course fee?
The Agentic AI course fee in Hyderabad typically ranges from ₹15,000 to ₹20,000, depending on the batch (online or offline) and additional support services. Call or WhatsApp +91 79959 25858 for payment options.
Is internship support available?
Yes. The program includes internship opportunities, and learners continue with internship support until they are placed. You work on real-time projects that build practical experience for your resume and interviews.
Is placement assistance available?
Yes. We provide full placement assistance, including resume building, LinkedIn profile support, mock interviews, interview preparation and job referrals to companies hiring Agentic AI professionals. Final hiring decisions depend on your skills, projects and interview performance.
What career roles can I target?
Learners can work towards roles such as AI Agent Developer, AI Engineer, Generative AI Engineer, LLM Application Developer, AI Automation Engineer and AI Solutions Engineer. Experienced professionals may target architect or consulting roles. The right fit depends on your background and portfolio.
What are the prerequisites?
Basic programming knowledge, logical thinking, comfort installing software and following technical instructions, and a willingness to practise. Familiarity with Python, APIs and basic AI concepts helps you learn faster but isn't mandatory.
How do I book a free demo?
Book a free demo with the form on this page, on WhatsApp at +91 79959 25858, or by calling the same number (Monday to Saturday, 9 am to 9 pm). In the demo you'll see the curriculum, meet a trainer and get advice on mode and batch timing, with no obligation.
Free demo class
Ready to Build Your First AI Agent?
Join a free demo and see how the course works before you decide. In one session you'll get a clear picture of:
Curriculum • Projects • Your trainer • Learning mode • Batch timing • Career path • Tools • Practical approach
No obligation. Speak with a counsellor before choosing the program.
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Learn for free
AI Learning Resources
Guides to help you understand agents before (and after) you enrol. All articles →
Agentic AI vs AI Agents
How the two terms differ and where multi-agent systems fit.
Read the guide →
Agentic AI Architecture Explained
The building blocks behind agents for automation.
Read the guide →
Agentic AI Examples
Real examples of agents in cloud, operations and business.
See examples →
AI Agent Development
What AI agent developers build and the skills involved.
Learn more →
Agentic AI Interview Questions
Common questions and how to answer them with your projects.
Practise now →
All Agentic AI Articles
Tutorials, roadmaps and explainers on agents, RAG and LLMs.
Browse the blog →
Free download
Download the Agentic AI Course Curriculum
Module-by-module syllabus with tools and projects. Prefer the bigger picture? Get the Agentic AI Career Roadmap 2026 too.
• Full module breakdown
• Project list
• Tools you'll use
Get in touch
Contact Agentic AI Masters
✉️ Email
agenticaimasters@gmail.com
📍 Address · Mon–Sat, 9 am – 9 pm
3rd floor, Dr. Atmaram Estates, near JNTU Metro Station, Hyder Nagar, Hyderabad, Telangana 500072
