Containerised API Service
A FastAPI service backed by PostgreSQL, typed with Pydantic and tested, shipped in Docker with CI running on every push — the foundation every later agent is built on.
- FastAPI
- Docker
Agentic AIAI-Powered Curriculum
Agentic AI Course in Phagwara
Learn Agentic AI in Phagwara — build LLM agents that plan, call tools and finish multi-step work with LangGraph & MCP, on live projects with placement support.
- Live client projects
- Practitioner trainers
- Placement support
- Certificate + internship
- Duration
- 3, 6 or 9 Months
- Mode
- Classroom, Weekend & 1-on-1
- Eligibility
- 12th Pass Onward
- Includes
- Internship Letter
25,000+
Students trained
since 2007
4.9★
Google rating
556+ reviews
100%
Practical training
live client work
Overview
Course overview
Agentic AI is software that pursues a goal instead of answering one prompt. A language model replies; an agent decides what to do next — it plans a step, calls a real tool such as an API, a database or a browser, reads the result and goes again until the job is done, the budget is spent or a human must approve. Techcadd’s Agentic AI Course in Phagwara teaches you to build, evaluate, secure and operate exactly those systems. The syllabus is a thirty-three-module ladder with exit points at three, six and nine months. The first stage starts at Python from the first line, so no programming background is assumed, then covers prompting and structured output, tool calling and the Model Context Protocol, retrieval-augmented generation with citations, memory and state, graph orchestration with human-in-the-loop approval, then evaluation, guardrails and a deployed capstone. The six-month stage adds async engineering, model routing, production MCP gateways, GraphRAG, durable execution, multi-agent systems, red teaming and Kubernetes deployment. The nine-month stage moves from building an agent to owning the platform — ingestion pipelines, vector infrastructure, evaluation as a service, fine-tuning, voice agents and governance. Every module ends in a graded artefact reviewed by a trainer who ships this work for clients, and you leave the Phagwara centre with agents you can explain end to end.
What you get
- 100% practical, project-based learning
- AI tools integrated into every module
- Live client projects under trainer supervision
- Internship letter and placement support
- Small batches with daily doubt clearing
- Students
- 25K+
- Google rating
- 4.9★
- Estd.
- 2007
- Practical
- 100%
EligibilityWho can do
Who can do
this course
The Agentic AI course is built for people at six different starting points, and the batch is deliberately mixed. What matters far more than your background is turning up consistently and finishing what each module asks you to build.
- 01
Students after 12th
Module 01 starts at Python from the first line, so nothing is assumed. Most students run the 3-month Practitioner stage alongside a degree at LPU, GNA University or Kamla Nehru College using the weekday or weekend batch, and extend later if it takes.
- 02
B.Tech, BCA and final-year students
A college project rarely gets an interview on its own. A published MCP server, a cited RAG assistant and a deployed agent with an evaluation report do — and the internship letter counts towards your industrial training requirement.
- 03
Working developers
If you already write Python or JavaScript, the first module is a fast revision and everything after it is new. The weekend batch lets you move from web or backend work into AI engineering without leaving your current job.
- 04
Business owners in Phagwara
Immigration consultancies, export houses, schools and hospitals along the GT Road all have repetitive multi-step work an agent could take on. Learn enough to build the first one yourself, or to judge properly what a vendor is selling you.
- 05
Freelancers and consultants
Agent builds are billed remotely, so a Phagwara address costs you nothing on a brief from Delhi, Dubai or Canada. The programme covers scoping, evaluation evidence and cost-per-task reporting, which is how you defend a price rather than just quote one.
- 06
Self-taught learners and restarters
If tutorials left you with notes but nothing running, what changes here is a graded deliverable at the end of every module and a trainer who reviews what you produced this week. A gap on the CV matters less than a deployed agent you can demonstrate.
The case for itWhy this programme
Why this programme
is worth your year
Businesses across Punjab are moving their AI budgets from chatbots that answer to agents that actually complete work, and there are very few people in the Doaba region who can build one, prove it works and keep it running safely. That gap is the whole argument for this course: there is local demand, there are budgets, and there are very few trained people to hand the work to.
Build skills that hold up beyond the classroom.
What separates this from a playlist of tutorials is supervision on real work. From the second half of the course you build on live client projects with a trainer beside you, make decisions that have consequences, and correct them the following week. That loop is the skill. No employer in Phagwara will take your word for it without work they can inspect.
Be realistic about the money. A fresher who finishes with a working portfolio starts near the bottom of the band and moves quickly; someone who finishes with a certificate and nothing to show does not. The difference is entirely what you built.
The alternative is what most people try first: free videos, a cheap online course, six months of drifting, and knowledge you cannot demonstrate. A structured programme with live projects, a mentor who corrects you, an internship letter and a placement cell that actually calls employers is the difference between knowing the subject and being hired to do it.
Students reach the Phagwara centre from Banga, Nakodar, Kartarpur and the university belt, and the weekend batch exists so a job or a degree does not have to be paused to attend.
- 01
One ladder, three honest exit points
Thirty-three modules in a single numbered sequence. Leave at three months with a deployed agent, at six as an engineer who can run one in production, or at nine as someone who can design the platform. A shorter track costs you scope, never depth.
- 02
Starts from zero, not from “assumes Python”
Most agentic AI courses quietly require a developer. Module 01 teaches Python, the command line, Git, HTTP and SQL from nothing, and everything after it assumes only what you built there.
- 03
Evidence over demos
Anyone can show an agent that works once. Half the grading weight sits on a labelled evaluation set, a measured before-and-after, a red-team result and a cost-per-task number — because that is what a hiring manager asks for.
- 04
One tool per layer, taught deeply
Frameworks change every quarter; tool calling, retrieval, state, evaluation and guardrails do not. You learn LangGraph, MCP, Qdrant and RAGAS properly and treat the alternatives as swappable implementations of the same idea.
- 05
Security is architectural, not a prompt
Any agent that combines private data, untrusted content and the ability to act outward is exploitable. You learn content isolation, least privilege, egress control and human confirmation before irreversible actions — from the first stage, not as an afterthought.
- 06
Capability-gated progression
You advance when a deliverable passes review, not when the calendar says so. That is slower for some students and faster for others, and it is why the certificate means something to the employers who know it.
Why now
Agents are where the AI budget is going — and the people who can build them are rare
- Tool calling, retrieval and long context turned agents from a research idea into deployable software in about two years; companies now have agents in production and very few people who can evaluate, secure and operate them.
- Every module ends in a graded artefact — a published MCP server, a cited RAG assistant, an approval-gated LangGraph agent — so the portfolio builds itself week by week.
- AI Agent Developer roles in Punjab start around ₹25,000 – ₹50,000 a month for a fresher with a working, deployed portfolio, and remote briefs pay on the client’s scale rather than the local one.
- The hiring signal has shifted from “can you prompt it” to “can you prove it works and say what it costs” — this programme is built around exactly that evidence.
SyllabusHands-on
What you will
actually build
The syllabus is arranged so every module produces an asset rather than a set of notes. You will cover programming foundations — from absolute zero, llm foundations, prompting & structured output, tool calling, function execution & mcp, retrieval-augmented generation & knowledge grounding, and finish with a live project built on Python, LangGraph, LangChain. Modules run in the order a real project runs: foundations first, then the core skills, then applied work under supervision, then the portfolio and interview preparation that turn all of it into an offer letter.
Agentic AI02/04
Core Skills
The working knowledge the job description actually lists.
Tool Calling, Function Execution & MCP
The step that turns a model into something that can do work — designing tools, calling them reliably and publishing them over the Model Context Protocol.
2 weeks · 10 sessions
Retrieval-Augmented Generation & Knowledge Grounding
Make an agent answer from your documents rather than its memory, with citations a reviewer can check and a score that proves it.
2 weeks · 10 sessions
Memory, State & Context Management
Give an agent memory that survives a session and stays private to the user it belongs to.
2 weeks · 8 sessions
Topics covered
Tool schemas, parameter design, parallel versus sequential callsRetries, idempotency and timeouts around tools that touch real systemsThe ReAct loop written from scratch, without a frameworkMCP servers, clients and transports with the Python SDK and MCP InspectorEmbeddings, chunking strategies and document parsing for PDFs and scansHybrid BM25-plus-dense search across Qdrant, Chroma and pgvectorReranking and clause-level citations with LlamaIndex and LangChainMeasuring faithfulness and context precision with RAGAS on a gold setShort-term, long-term and episodic memory, and rolling summarisationLangGraph checkpointers, Mem0 and Zep for persistent stateMulti-user isolation with a test that proves no tenant sees another’s dataPII handling, retention rules and a delete endpoint built on Presidio
Programme length
Choose the duration that suits you
The Agentic AI course runs as three nested levels. Each one builds on the last, so you can start at the foundation and continue later without repeating anything.
- 3 MonthsAgent Practitioner
Build and deploy your first production agent
Start from no programming experience and finish with a deployed agent, a published MCP server and a cited RAG assistant — Modules 01 to 07 of the ladder.
What it covers
- Programming foundations — Python, Git, HTTP, SQL and Docker
- LLM foundations, prompting and structured output
- The workflow-versus-agent decision framework
- Tool calling, the ReAct loop and MCP servers
- Retrieval-augmented generation with hybrid search and citations
- Memory, state and multi-tenant isolation
- LangGraph orchestration and human-in-the-loop approval
- Evaluation, guardrails and a deployed capstone
Skills & tools
- Python
- FastAPI
- Pydantic
- LangGraph
- MCP
- Qdrant
Recommended for
Agent Developer, AI Automation Engineer, Solutions Engineer and junior AI developer roles.
- 6 MonthsAgent Engineer
Make it work every time, for many users, at a defensible cost
Everything in the Practitioner stage, then Modules 08 to 20 — the engineering that turns a working demo into a system a business can rely on.
What it covers
- Async Python and typed LLM client libraries
- LLM internals, model routing and self-hosted serving
- Advanced prompt and context engineering with DSPy
- Production MCP servers with OAuth and code sandboxes
- Advanced retrieval, hybrid search and GraphRAG
- Memory architecture for long-lived agents
- Durable orchestration with Temporal
- Multi-agent systems and communication protocols
+ 5 more
Skills & tools
- LangGraph
- vLLM
- DSPy
- Temporal
- Kubernetes
- LangSmith
Recommended for
AI Engineer, LLM Application Developer, RAG Engineer and AI Platform Engineer roles.
- 9 MonthsAgent Architect
Own the platform ten teams build agents on
Both earlier stages, then Modules 21 to 33 — from building one agent to designing, securing and governing the platform an organisation runs them on.
What it covers
- Data engineering and CDC knowledge pipelines with ACL propagation
- Retrieval at scale and vector infrastructure
- GraphRAG, knowledge graphs and agentic retrieval
- Memory architecture and continual learning
- Multi-agent systems, A2A and interoperability
- Browser fleets and autonomous coding agents
- Evaluation science and benchmarking
- Security architecture, red teaming and threat modelling
+ 5 more
Skills & tools
- Qdrant
- Neo4j
- Hugging Face
- Kubernetes
- Terraform
- LiveKit
Recommended for
Agentic AI Architect, Staff AI Engineer, AI Platform Engineer and Head of AI Engineering pathways.
What changes with each duration
Python programming from zero
- 3 Months
- 6 Months
- 9 Months
LLM APIs & structured output
- 3 Months
- 6 Months
- 9 Months
Tool calling & MCP servers
- 3 Months
- 6 Months
- 9 Months
Retrieval-augmented generation
- 3 Months
- 6 Months
- 9 Months
Memory & state management
- 3 Months
- 6 Months
- 9 Months
LangGraph orchestration & human-in-the-loop
- 3 Months
- 6 Months
- 9 Months
Evaluation, guardrails & deployment
- 3 Months
- 6 Months
- 9 Months
Async engineering & model routing
- 3 Months
- 6 Months
- 9 Months
Production MCP gateways
- 3 Months
- 6 Months
- 9 Months
GraphRAG & advanced retrieval
- 3 Months
- 6 Months
- 9 Months
Durable & multi-agent orchestration
- 3 Months
- 6 Months
- 9 Months
Browser & coding agents
- 3 Months
- 6 Months
- 9 Months
Red teaming & agent security
- 3 Months
- 6 Months
- 9 Months
Kubernetes deployment & cost engineering
- 3 Months
- 6 Months
- 9 Months
Data pipelines & vector infrastructure at scale
- 3 Months
- 6 Months
- 9 Months
Fine-tuning & RL post-training
- 3 Months
- 6 Months
- 9 Months
Voice & multimodal agents
- 3 Months
- 6 Months
- 9 Months
Agent platform, governance & FinOps
- 3 Months
- 6 Months
- 9 Months
The programme is nested, not parallel. The 3-month Practitioner track covers Modules 01 to 07 and ends with a deployed agent. The 6-month Engineer track contains all of that and continues from Module 08 into production engineering, security and scale. The 9-month Architect track contains both and continues from Module 21 into platform design, post-training and governance — so choosing a shorter track costs you scope, never depth, and you can extend later without repeating a single module.
The toolchain behind the craft
One course.
A mesh of real tools.
Everything below is installed on the lab machines and used on live client work, not shown once in a slide and forgotten.
- Python
- LangGraph
- LangChain
- Claude
- OpenAI API
- Gemini
- Model Context Protocol (MCP)
- FastAPI
- Pydantic
- Qdrant
- pgvector
- LlamaIndex
- RAGAS
- Docker
- Git & GitHub
Certification
Get Certified in Agentic AI
Complete the course with a portfolio of live projects and receive an industry-recognised certificate, plus a documented internship letter accepted by Punjab universities.
- Industry Certificate
Recognised by employers across Punjab and beyond
- Internship Letter
Based on real client work, not a simulation
- Portfolio of Projects
Live work you can show in any interview
- Placement Support
CV review, mock interviews and hiring drives
Two certificates on completion — the course certificate and a separate capstone project certificate.
Future scopeWhere this course
Where this course
takes you
The roles this opens, what they pay in Punjab and beyond, and who is hiring for them — drawn from published job-market listings on one comparable scale, not a brochure number.
Salary outlook
AI Agent Developer
Builds, evaluates and operates LLM agents that call tools, retrieve from company data and complete multi-step work. Earnings depend on your portfolio, evaluation evidence, production experience, employer and location.
- Starting package
- ₹25,000–₹50,000/month
- After 2 years
- ₹50,000–₹1,00,000/month
Punjab — AI Agent Developer
Fresher₹25,000–₹50,000/month
After 2 years₹50,000–₹1,00,000/month
Delhi / NCR — AI Engineer
Fresher₹35,000–₹70,000/month
After 2 years₹80,000–₹1,60,000+/month
Remote / Freelance Agent Work
Fresher₹20,000–₹45,000/month
After 2 years₹70,000–₹1,80,000+/month
Indicative ranges for AI Agent Developer roles, compiled from public job-market listings and drawn on the same scale in every market. Actual offers vary by employer, skillset and interview performance — Punjab pay typically reaches 2× the fresher ceiling within two years of delivery experience.
Talk about your target roleWhere AI Agent Developer graduates get hired
- IT services and product companies adding agent features to existing software
- AI startups building copilots, assistants and autonomous workflows
- Consulting and automation firms delivering agents for enterprise clients
- Local businesses — immigration, education, healthcare and export — automating multi-step work
- Remote clients and freelance platforms hiring for LangGraph, MCP and RAG builds
- 01
What job roles open up after this course?
The three-month stage prepares you for Agent Developer, AI Automation Engineer and Solutions Engineer roles. The six-month stage prepares you for AI Engineer, LLM Application Developer, RAG Engineer and AI Platform Engineer. The nine-month stage prepares you for Agentic AI Architect and staff-level AI engineering. Each level is interviewed differently — reliable tool wiring and a deployed demo early on; evaluation, cost control and incidents in the middle; system design, threat modelling and total cost of ownership at architect level.
- 02
What can I earn, and how fast does it grow?
A fresher with a working, deployed portfolio starts around ₹25,000 – ₹50,000 a month in the Punjab market and reaches ₹50,000 – ₹1,00,000 with two years of delivery experience. Delhi/NCR runs materially higher, from ₹35,000 – ₹70,000 for a fresher to ₹80,000 – ₹1,60,000+ after two years.
- 03
Can I freelance or work remotely with this skill?
Yes, and it is one of the most remote-friendly skills in the catalogue. Freelance agent work starts lower, around ₹20,000 – ₹45,000 a month, because income ramps rather than starting at a salary, but it reaches ₹70,000 – ₹1,80,000+ once you have delivered real builds. The programme covers scoping, evaluation evidence and cost reporting so you can price and defend the work.
- 04
Which industries hire for this in Punjab?
IT services and product companies adding agent features, AI startups, consulting and automation firms, and — increasingly — the immigration consultancies, schools, hospitals, export houses and real-estate firms around Phagwara, Jalandhar and Kapurthala that have repetitive multi-step work worth automating. Remote clients in Delhi, Dubai and Canada make up a large share on top.
- 05
Do I need to know Python before joining?
No. Module 01 teaches Python from the first line, along with the command line, Git and GitHub, HTTP and REST, and SQL, and ends with a containerised FastAPI service with tests passing in CI. Everything after it assumes only that, which is why the programme is open to graduates from any stream and to career changers.
PortfolioHands-on projects
Hands-on projects
you will ship
Document Extraction Engine
Unstructured invoices and contracts converted into schema-valid JSON through Pydantic repair loops, with under 2% validation failure across a hundred documents.
- Pydantic
- Claude API
Published MCP Server
Five or more scoped tools with schema documentation and integration tests, plus a ReAct loop written by hand that uses them without any framework.
- Tool Calling
- ReAct Loop
Cited Compliance Copilot
A hybrid-search RAG assistant with clause-level citations, scoring 0.85+ faithfulness and 0.80+ context precision on a fifty-question gold set.
- RAG
- RAGAS Evaluation
Human-in-the-Loop Approval Agent
A stateful LangGraph agent that pauses for a human sign-off, streams every step to the user, and resumes cleanly from a checkpoint after a forced crash.
- LangGraph
- PostgreSQL
Multi-Provider Model Router
A cascading router across Claude, OpenAI, Gemini and a self-hosted vLLM endpoint with automatic fallback, benchmarked on cost, quality and latency across five models.
- LiteLLM
- vLLM
200-Attack Red-Team Report
A threat model for a tool-using agent, an automated prompt-injection suite of two hundred attacks, a measured before-and-after mitigation result and an incident runbook.
- Garak
- PyRIT
Deployed Support Agent Capstone
A publicly reachable support agent with CRM write-back, human escalation, a CI regression gate, an evaluation report, an architecture diagram and a cost-per-conversation figure.
- LangGraph
- Docker
The working loop
Learn it. Build it. Make it yours.
Every project moves through the same loop: understand the brief, build with guidance, then explain the decisions behind your work.
- 01
Understand
Break a real requirement into a clear plan and the right tools.
Containerised API Service
- 02
Build
Work hands-on with trainer feedback while the decisions are still easy to change.
Document Extraction Engine
- 03
Present
Turn the finished work into a portfolio story you can defend in an interview.
Published MCP Server
Why techcadd
Why students choose techcadd
There are many places to learn this in Phagwara and the brochure syllabus looks similar at all of them. What differs is who teaches, whether you ever touch real work, and whether anyone picks up the phone after you have paid. techcadd has trained students across Punjab since 2007 on the same model: small batches, working practitioners as trainers, client projects as coursework.
Trainers who still do the work
Your trainer is not a full-time lecturer. They deliver client projects for techcadd’s services arm, so examples in class are current rather than a case study from five years ago.
Live projects, real consequences
You work on genuine client requirements under supervision. This is where a portfolio comes from, and it is the first thing an interviewer asks to see.
Small batches and open lab hours
Batches stay small enough that a trainer sees your screen daily. Lab time runs outside class hours and doubt sessions continue until the concept lands.
Internship letter and certificate
Every student finishes with an industry-recognised certificate and a documented internship on real work, accepted for university industrial training requirements.
A placement cell that persists
Mock interviews, CV reviews and drives with hiring partners across Phagwara, Jalandhar and North India, repeated after a rejection, not abandoned.
Since 2007, 25,000+ students
Nearly two decades of hiring relationships in Punjab is why a call from our placement cell gets answered and why local employers know what our certificate means.
Student reviewsWhat our students
What our students
in Phagwara say
- Google
What made it click for me was the lab time. You can sit after class and someone will still explain it until you get it.
KMKaran MehtaB.Tech Student · Phagwara - Google
I travelled in for the weekend batch and it was worth every trip. Small batch, real work, no time wasted on theory nobody uses.
ASArshdeep SinghTrainee Engineer · Adampur - Google
The course got me interview-ready faster than I expected. My interviewer asked to see my project and that was the whole conversation.
PRPooja RaniGraduate · Kartarpur
- Google
I was switching careers and worried I would be behind. Half the batch was doing the same thing, and nobody made it awkward.
SKSimran KaurCareer Switcher · Phagwara - Google
I joined with almost no background and finished with a project I could actually show. The trainer never rushed the basics.
RSRohit SharmaBCA Student · Banga - Google
techcadd’s placement cell kept calling me for drives until I was placed. That persistence mattered more than anything else.
NKNavjot KaurPlaced · Jalandhar
Got questions?
Frequently Asked Questions
It is a programme for building AI systems that pursue a goal on their own — planning a step, calling a real tool such as an API, database or browser, reading the result and going again until the job is done or a human needs to approve. Four properties make something an agent: goal-directedness, tool use, memory and autonomy. You learn to build, evaluate, secure and operate those systems on one 33-module ladder with exit points at three, six and nine months.
Get started today
Not sure if Agentic AI is the right fit?
One call with a counsellor is usually enough to find out. Book a free demo class and see the lab before you decide.
Course information
Ask about Agentic AI
Send your question and a counsellor will call you back about batch timings, fees, EMI options, placement record, or whether this course fits your degree.
- Free counselling and demo class
- Weekday, evening, weekend or 1-on-1, all 2-hour classes
- Internship letter and placement support