What you’ll learn
By the end you won’t just know how to useAI agents — you’ll know how to build them, customise them and deploy them to solve real business problems.
Build a complete AI agent framework in Python
Create every component yourself — loop, memory, tools, environment — so you understand exactly how agents work instead of memorising someone else's API.
Design tool discovery and function calling
Build the mechanisms that let your agents interact with external systems, pick the right tool and perform meaningful actions on real data.
Ground agents in your own data with RAG
Chunk, embed, index and retrieve — then wire retrieval in as a tool so your agent answers from your documents with citations instead of hallucinating.
Ship production-ready agents
Practical agents for intelligent file exploration, documentation generation and coding — with logging, guardrails and a cost-per-answer you can defend.
Master the GAME and GAIL frameworks
Goals, Actions, Memory and Environment for design; Goals, Actions, Instructions and Limits for prompts. Design the agent before you build it.
Stay framework-independent
Learn the principles underneath LangChain, LlamaIndex and the rest — so you can use any of them, debug all of them, and depend on none of them.
Why principles matter more than frameworks
The AI landscape changes weekly, but the core principles of agent design stay constant. By building agents from scratch you gain knowledge that outlives whatever is trending.
Transferable knowledge
Works across any LLM or AI technology — OpenAI, Claude, Gemini, Llama, whatever comes next.
Deep debugging skills
You understand what happens at every level, so you can fix what other people can only report.
Framework independence
Free from dependence on third-party libraries — and able to succeed with any of them.
Future-proof expertise
Still relevant when today’s popular tools are long forgotten.
Course syllabus
Five modules, 53lessons. RAG isn’t bolted on as a final module — retrieval is a tool and a vector store is memory, so 10 RAG lessons sit inside the modules where they actually belong.
5 modules · 53 lessons
Learn the core concepts behind agentic AI — systems that plan, act and adapt based on feedback. You will explore patterns like flipped interaction, agent loops and programmatic prompting, and see how memory and structured outputs let an agent operate autonomously instead of waiting for instructions.
Tip: Focus on how the agent decides what to do next. That decision-making loop is the foundation of everything you build later.
- 1What makes software "agentic" — assistants vs. agents
- 2The agent loop: perceive → reason → act → observe
- 3Programmatic prompting: calling an LLM as a function, not a chat
- 4The flipped interaction pattern — let the agent ask you
- 5Structured outputs: JSON, schemas and why free text breaks agents
- 6Memory types: scratchpad, conversation, episodic and long-term
- 7Feedback and adaptation — how an agent corrects its own course
- 8Grounding an agent in your own data: why RAG existsRAG
- 9Embeddings and vector similarity in plain EnglishRAG
- 10Lab: build a minimal agent loop in Python from an empty file HANDS-ON
You’ll walk away with: A working Python agent loop that reasons, acts and observes — under 100 lines, no framework.
Skills you’ll gain
Tools you’ll learn
Python 3
OpenAI API
Git & GitHubWho this course is for
- Python developers who want to move into AI engineering
- Final-year students and freshers targeting AI/ML internships in India
- Data analysts and data scientists adding GenAI to their stack
- Backend and full-stack engineers building LLM features at work
- Anyone who has used ChatGPT and now wants to build with it
What you need first
- Comfortable writing Python functions, loops and classes
- Basic command line and Git — clone, commit, push
- No machine learning, maths or deep learning background needed
- An OpenAI API key with paid access (a few dollars covers the whole course)
This course teaches these concepts using OpenAI’s APIs, which require paid access. The principles and techniques adapt directly to any other LLM with tool calling.
Finish the course, earn a certificate you can share
Complete all five modules and submit your capstone agent to receive a verifiable MyInternships.in course completion certificate. Add it to your LinkedIn profile, your resume and your portfolio — alongside the three working agents you built.
- Verifiable certificate with a unique ID
- Add directly to your LinkedIn profile
- Backed by 3 portfolio projects + capstone
- Recognised by employers hiring for AI roles
Where this course takes you
Agent and RAG experience is one of the fastest-growing hiring requirements in Indian tech. Typical ranges for roles this course prepares you for:
Indicative ranges based on publicly advertised roles in India. Actual compensation varies by employer, location and experience.
Frequently asked questions
What is agentic AI, and how is it different from using ChatGPT?+
ChatGPT waits for you to ask. An agentic AI system sets out to achieve a goal: it plans, calls tools, observes what came back, and decides its next step on its own. This course teaches you to build that loop in Python — goals, actions, memory and environment — rather than just prompting a chatbot.
What is RAG and why is it part of an agentic AI course?+
RAG (retrieval-augmented generation) grounds an LLM in your own documents so it answers from real sources instead of guessing. Inside an agent, retrieval is simply a tool the agent can call and a vector store is a form of memory — which is why we teach RAG inside every module instead of isolating it at the end.
Is this Online Agentic AI & RAG Course free?+
The full syllabus, module notes and learning path are free to follow on MyInternships.in. Register your interest above and we will send you the lesson releases, project starter code and a mentor-led cohort invite when the next batch opens.
Do I need machine learning or deep learning experience?+
No. You need working Python — functions, loops, classes — and that is it. You will not train models or touch linear algebra. You will build software that orchestrates models, which is a software engineering skill.
Which frameworks does the course use — LangChain, LlamaIndex, CrewAI?+
None of them, deliberately. You build every component yourself so you understand what those frameworks do under the hood. Graduates report that picking up LangChain or LlamaIndex afterwards takes an afternoon, because there are no black boxes left.
Do I need to pay for an OpenAI API key?+
Yes — the course is taught with OpenAI's APIs, which require paid access. Course exercises typically cost a few dollars in total. Every principle and technique transfers directly to Claude, Gemini, Llama or any other LLM with tool calling.
How long does the course take to finish?+
About 24 hours of content across 5 modules. Most learners finish in 4 to 6 weeks studying an hour a day, and leave with three portfolio projects plus a capstone agent.
What jobs can I apply for after this course?+
AI Agent Developer, LLM/AI Engineer, GenAI Application Developer and Machine Learning Engineer roles. Agent and RAG experience is one of the fastest-growing hiring requirements in Indian tech — and you can apply to AI internships and fresher jobs on MyInternships.in the day you finish.
Will I get a certificate?+
Yes. Complete the five modules and submit the capstone agent to receive a shareable MyInternships.in course completion certificate you can add to your LinkedIn profile and resume.
Start building agents that actually do things
Request access below. Every enrolment is approved manually so the cohort stays small and we can actually help you — most requests are approved within a working day.
Request access — free
Registered members only. Manually approved.