Agentic AI & AI Agent Development — Full-Stack Course
From zero to deployed AI agents: design, build, evaluate, monitor, and ship production-grade AI agents and RAG-based LLM applications, end to end.
Build, evaluate, and ship real AI agents
- Build production-grade RAG pipelines grounded in real documents — not plausible-sounding guesses
- Design and orchestrate multi-step AI agents with tool use, memory, and multi-agent hand-offs
- Ship full-stack agent apps: FastAPI + React + Postgres/pgvector, end to end
- Evaluate LLM systems rigorously with automated eval pipelines (RAGAS, LLM-as-judge, golden datasets)
- Test, monitor, and deploy AI systems in production with CI/CD, tracing, and cost/security guardrails
The Curriculum
Both Track A and Track B follow this identical module structure.
0 Module 0 — Python & AI/ML Foundations
Python refresher (OOP, async, envs), NumPy/Pandas, core ML concepts, and your first scikit-learn text classifier.
1 Module 1 — LLM Foundations
Transformers, tokens & context windows, prompting patterns (zero/few-shot, CoT, ReAct), structured outputs and function calling.
2 Module 2 — Retrieval-Augmented Generation (RAG)
Chunking strategies, embeddings & vector DBs (FAISS, Chroma, pgvector), hybrid search, re-ranking, and an end-to-end RAG pipeline with citations.
3 Module 3 — Agentic AI Architecture
ReAct, Plan-and-Execute, Reflexion; tool/function calling design; agent memory; multi-agent orchestration with LangGraph; guardrails and human-in-the-loop.
4 Module 4 — Full-Stack Integration
FastAPI backends, async streaming, a React/Next.js or Streamlit chat UI, auth, and Postgres + pgvector.
5 Module 5 — Evaluation (Eval) Frameworks
RAGAS, DeepEval, promptfoo, LLM-as-judge patterns, golden datasets, and an automated eval pipeline that gates deployment.
6 Module 6 — Testing for AI Systems
Unit testing deterministic components, testing non-deterministic LLM outputs, contract testing, and load testing with Locust/k6.
7 Module 7 — Monitoring & Tracing
LangSmith / Langfuse / OpenTelemetry tracing, cost and latency metrics, hallucination tracking, and alerting on drift.
8 Module 8 — Deployment & MLOps/LLMOps
Docker, CI/CD with GitHub Actions, prompt/model versioning, autoscaling, cost governance, and secrets management.
9 Module 9 — Capstone Project
Design, build, evaluate, monitor and deploy one end-to-end agentic system — presented with architecture, eval report, and live deployment.
Track Comparison
| Track A — Course Only | Track B — Course + Internship | |
|---|---|---|
| Fee | ₹35,000 | ₹60,000 |
| Format | Weekend batch, 2 hrs/day (Sat & Sun), theory + live-code walkthroughs | Track A, plus daily 1 hr/day (Mon–Fri) project mentorship & lab time |
| Duration | 3 months · 12 weeks · 24 sessions · ~48 hrs | 3 months · Track A sessions + ~60 hrs weekday mentorship |
| Capstone | Self-directed capstone project | Mentored, individually guided Capstone project |
| You receive | Course Completion Certificate | Internship Certificate + Experience Certificate + mentored Capstone + Course Completion Certificate |
| Best for | Self-directed learners who want the curriculum | Learners who want resume-ready, certified proof of work |
Seats are limited to 10 for this batch, for hands-on lab quality. Both tracks follow the identical module curriculum — the choice is depth of mentorship and certification, not content.
Who this course is for
- Working knowledge of Python
- Basic understanding of REST APIs
- No prior ML/LLM experience required for Module 0 — though pace will be brisk for complete beginners
Built for freshers looking to break into AI engineering, and for 3+ year IT professionals upskilling into agentic AI and LLM application development.
Apply for the October 3, 2026 Batch
Fill in your details and our team will call you within 1 business day to confirm your seat, track, and payment details. Only 10 seats available for this batch.
Frequently Asked Questions
Do I need prior AI/ML experience?
No. You need working knowledge of Python and basic REST API familiarity. Module 0 covers the ML/Python foundations everyone needs before diving into LLMs and agents.
What's the difference between Track A and Track B?
Both tracks follow the identical 10-module curriculum. Track B adds daily Mon–Fri mentorship, a guided individual Capstone project, and an Internship Certificate + Experience Certificate on top of the Course Completion Certificate.
What are the batch timings?
Weekend batch: Saturday & Sunday, 2 hours/session, for 12 weeks (24 sessions, ~48 hours). Track B adds 1 hour/day, Monday–Friday, of project mentorship.
Where is the course held?
In-person, at our Sholinganallur, Chennai location. Batch size is capped at 15–20 for hands-on lab quality — the upcoming batch has 10 seats.
What do I need to bring?
A laptop capable of running Python 3.11+ and a modern code editor (VS Code recommended). We'll guide you through environment setup (venv/uv) in the first session.
Is there a certificate?
Yes. Track A includes a Course Completion Certificate. Track B additionally includes an Internship Certificate and Experience Certificate tied to your mentored Capstone project.
What's the refund/cancellation policy?
See our full Refund & Cancellation Policy. In short: seats are confirmed on a first-apply, fee-paid basis, and refunds follow the timelines listed there.