Async LLM Client Library
Rate-limited, retried, cached and fully typed, with 90%+ test coverage and a resumable 100,000-record enrichment run behind it.
- asyncio
- httpx
- Tenacity
- pytest
Best After 12th 6-Month Agentic AI Program in Phagwara
Rated on Google4.9556+ reviews
A complete six-month program that turns you from a beginner into someone who can build, secure and deploy AI agents that work reliably for real users at a manageable cost.
Agentic AI Course in Phagwara
This 6-month program covers the complete journey from Python basics to production-ready AI agents. The first half builds your foundation — Python from the first line, the command line and Git, HTTP and APIs, how language models actually work, prompting, tool calling, RAG, memory and graph orchestration. The second half is the professional half: async engineering, model routing and self-hosted serving, DSPy optimisation, production MCP gateways, GraphRAG, durable execution, multi-agent systems, browser and coding agents, evaluation harnesses, red teaming, Kubernetes deployment and unit economics.
Key Highlights
- Duration:
- 6 Months
- Eligibility:
- 12th Pass (Any Stream)
- Mode:
- Classroom, Weekend & 1-on-1
- Includes:
- Certificate + Placement Support
Overview
Course overview
This 6-month program covers the complete journey from Python basics to production-ready AI agents. The first half builds your foundation — Python from the first line, the command line and Git, HTTP and APIs, how language models actually work, prompting, tool calling, RAG, memory and graph orchestration. The second half is the professional half: async engineering, model routing and self-hosted serving, DSPy optimisation, production MCP gateways, GraphRAG, durable execution, multi-agent systems, browser and coding agents, evaluation harnesses, red teaming, Kubernetes deployment and unit economics. You advance when your work passes review, not when the calendar says so. Every topic specifies the same four things — subjects in teaching order, the named tool stack, the commercial problem it solves and the project you keep.
What You’ll Learn
Every module ends in something you have built and a trainer has reviewed, so the list below is work you will have done rather than topics you will have heard about.
- 01
Reliability engineering, not prompt tips
bounded concurrency, circuit breakers, dead-letter queues and resumable checkpoints
- 02
A 200-attack red-team report grading your own agent before and after mitigation
- 03
Durable execution that survives a deploy, resuming exactly where it stopped
- 04
Cost per successful task, reduced by routing, caching and small-model substitution
- 05
A GraphRAG retrieval service with an ablation study quantifying each component
- 06
A live multi-user capstone with an eval dashboard, an SLO, a cost model and a public write-up
SyllabusHands-on
Course Curriculum
The syllabus is arranged so every module produces an asset rather than a set of notes. You will cover month 1 — python, llm prompting & tool calling, month 2 — rag, memory & agent frameworks, month 3 — evaluation, guardrails & a deployed agent, month 4 — production python, model routing & prompt optimisation, and finish with a live project built on Python & asyncio, Git, GitHub & Docker, FastAPI & PostgreSQL. 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.
Month 3 — Evaluation, Guardrails & a Deployed Agent
Evidence is the deliverable, and the agent goes live.
4 weeks · 24 sessions
Topics covered
Building evaluation datasets from real examplesDeterministic tests vs LLM-as-judge; trajectory and tool-choice evaluationRAG metrics: faithfulness, answer relevance and context recallRegression gates in CI; tracing tokens and costSecurity: prompt injection, input and output guardrails and PII filteringRefusal and escalation policiesPackaging an agent as a service: streaming APIs, rate limiting and secrets managementChat UI assembly with Streamlit; logging, alerting and monitoringArchitecture diagram and demo video
The toolchain
Tools you will actually work in
Everything below is installed on the lab machines and used on live client work, not shown once in a slide and forgotten.
- Python & asyncio
- Git, GitHub & Docker
- FastAPI & PostgreSQL
- Claude & OpenAI APIs
- Pydantic & Instructor
- MCP SDK & FastMCP
- LangChain, LlamaIndex & LangGraph
- Qdrant, Chroma, FAISS & Weaviate
- Neo4j & GraphRAG
- RAGAS, promptfoo & DeepEval
- LangSmith, Langfuse & Braintrust
- DSPy
- LiteLLM, vLLM & Ollama
- Temporal & Prefect
- Playwright & Browser Use
- Garak, PyRIT & Lakera Guard
- Kubernetes, Helm, Terraform & ArgoCD
- Prometheus & Grafana
Eligibility
Who can do this course
- 01
Students straight after 12th
Join from any stream. Six months is the shortest honest span in which a complete beginner reaches an AI Engineer job description, and the weekday and weekend batches both run the full ladder.
- 02
Graduates and final-year students
If you are finishing a BCA, B.Sc or B.Tech, this is the version of the programme that changes which interviews you are invited to. You arrive with a red-team report and a Kubernetes deployment, not a course certificate.
- 03
Working developers
If you already write Python, the foundation topics are quick and the professional topics are the real value. Containers, evaluation, security and cost engineering are what separate a developer from someone who can also ship and run what they wrote.
- 04
Career changers
The weekend batch exists for people already earning. Six months of evenings and Saturdays is enough to move into AI engineering without leaving your current job first.
The case for itWhy this programme
Why this programme
is worth your year
Companies have agents in production and almost nobody who can evaluate, secure and operate them — which is currently the most valuable gap an AI engineer can close. 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
Reliability, not just capability
Async concurrency, rate limits, retries with jitter, circuit breakers, resumable checkpoints and dead-letter queues. This is the difference between a notebook demo and a batch job that survives the night.
- 02
Cost is an engineering problem
Model routing sends trivial work to a small local model and only hard reasoning to a frontier one — the standard route to a 60–80% inference-cost reduction with no measurable quality loss.
- 03
Attack it before someone else does
You run a 200-attack automated red-team suite against your own agent, with a before-and-after mitigation report. Prompt injection is the defining unsolved risk of tool-using systems, and defence is architectural.
- 04
Multi-agent, measured honestly
You do not assume more agents are better. You build the system, then report head-to-head against a single-agent baseline on cost, latency and success rate — which is the question a reviewer will ask.
Why now
An Agent That Works in a Demo and One That Works in Production Are Different Software
- The second one has bounded concurrency, a routing layer, durable state that survives a deploy, typed contracts between components, an evaluation suite gating every release and a cost figure per successful task.
- That gap is currently the most valuable thing an AI engineer can close.
- Companies have agents in production and almost nobody who can evaluate, secure and operate them.
- An engineer with a production agent, an evaluation platform and a security report typically starts around ₹35,000 – ₹70,000 per month in this market, and more in remote roles.
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 scope
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, not a brochure number.
Mid level, and the most common destination from this course. Interviews test evaluation methodology, cost control and production incidents you have personally handled. Show the model router, the red-team report and the Kubernetes deployment with an SLO.
Senior-adjacent. Interviews test multi-tenancy, durable execution, gateway design and observability at scale. Show the durable procurement agent and the OAuth-protected MCP gateway.
The role that connects agents to the systems a company already runs. The production MCP section is exactly this job — scoped tools, audit logs and per-user permissions rather than a shared API key.
On-call for agents. Tracing, drift detection, shadow deployment, canary releases and the cost dashboard — the work this course grades you on in the evaluation, deployment and cost-engineering sections.
- 05RAG & Retrieval Engineer
- 06AI Security Engineer
Salary outlook — AI Engineer
Builds, evaluates, secures and operates agents in production. Agent engineering is currently one of the few fields where supply is well behind demand.
- Starting
- ₹35,000–₹70,000/month
- After 2 years
- ₹80,000–₹1,60,000/month
Indicative ranges compiled from public job-market listings. Actual offers vary by employer, skillset and interview performance.
Who is hiring for this
- Product companies running agents in production and needing them operated
- IT services and consultancies building agent automation for clients
- Platform teams inside larger businesses integrating AI into existing systems
- Remote roles, where agent engineering supply is well behind demand
Portfolio
Hands-on projects you will ship
Multi-Provider Model Router
A cascading router with automatic fallback and a self-hosted vLLM endpoint, benchmarked across five models on cost, quality and latency.
- LiteLLM
- vLLM
- OpenRouter
GraphRAG Retrieval Service
Hybrid search plus reranking plus a knowledge graph, with an ablation study quantifying what each component actually contributed.
- Neo4j
- GraphRAG
- ColBERT
Durable Procurement Agent
A multi-day workflow that survives a forced restart mid-execution and correctly compensates a partially completed order.
- Temporal
- LangGraph
- Redis
200-Attack Red-Team Report
A full threat model with an automated injection suite and a measured before-and-after mitigation result, plus an incident runbook.
- Garak
- PyRIT
- Lakera Guard
Kubernetes Agent Deployment
An autoscaled, canary-released platform defined in Terraform, with a load-test report and a published SLO and error budget.
- Kubernetes
- Helm
- Terraform
- ArgoCD
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
Take a real requirement apart before touching a tool — what is being asked, what it needs, and which part to build first.
Async LLM Client Library
- 02
Build
Work hands-on with your trainer watching the screen, so a wrong turn is caught in the same session rather than three weeks later.
Multi-Provider Model Router
- 03
Present
Walk through what you built and why you built it that way. This is the interview rehearsal, run against every project rather than once at the end.
GraphRAG Retrieval Service
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.
Progress based on skill, not calendar
You advance when a deliverable passes review. Nobody reaches the capstone without every previous topic actually passing.
Real infrastructure, licensed and supervised
Kubernetes clusters, self-hosted vLLM endpoints and live model APIs with per-student budgets — so the load test is a load test, not a thought experiment.
Trainers who still ship
The people teaching Temporal workflows and MCP gateways are the people writing them for client work, which is why the failure sections cover failures that actually happen.
A ladder you can extend
Finish here at 6 months, or continue into the 9-month Architect track later. The extension picks up exactly where this course ends — you never repeat what you have already passed.
FAQs
Frequently asked questions
Find answers to the questions students ask before enrolling.
Six months in total. The first three months cover the foundation topics and the next three months cover the professional topics. Weekday, evening and weekend batches cover the same syllabus, and 1-on-1 training is available. Every class runs for 2 hours.
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
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.