Systems that act, not just answer
Tool use, planning loops, memory, multi-agent orchestration and the guardrails that make an autonomous system safe enough to run against real data.
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Course overview
The Agentic AI course in Ludhiana at techcadd covers what happens after a model can answer: tool definitions, agent loops, planning, memory, error recovery, human approval checkpoints and multi-agent patterns that do not quietly destroy something.
An agent is a language model that can act — call your functions, query your database, send your email, update your records. That single change turns every mistake from an embarrassing answer into an actual consequence, which is why this is the most engineering-heavy course in our AI track and why it is not the one to start with.
You will build agents that complete real multi-step work and, more importantly, agents that fail safely: retries with backoff, tool errors handled rather than swallowed, budget and step limits, an audit trail of what was done, and human approval in front of anything irreversible. Multi-agent patterns are taught with their costs stated plainly, because most problems that look like they need five agents need one agent and a better tool.
Prerequisite is real: Python plus some experience calling an LLM API. Take the Generative AI course first if either is new — students who skip that step spend this course learning the previous one.
Curriculum
4 modules and 22 topics across 3 months, in the order they are taught. Each one closes with something that works before the next one opens.
What separates an agent from a chatbot, and where the pattern genuinely earns its complexity.
Topics covered
Tools & libraries
You finish with
A minimal agent loop you wrote yourself, with a hard step limit.
Defining tools a model can call reliably, and handling what happens when a call fails.
Topics covered
Tools & libraries
You finish with
A five-tool agent that recovers cleanly from every failure mode you test.
Giving an agent the context to work across steps, sessions and documents.
Topics covered
Tools & libraries
You finish with
An agent that completes a multi-session task without losing what it already did.
Orchestration, review agents, approval gates and running the whole thing in production.
Topics covered
Tools & libraries
You finish with
Your capstone multi-agent system deployed with a full audit trail and approval gates.
The part most courses skip
Any tutorial can show you an agent that works. This section of the agentic AI training is about the far more common case, and it is the reason the course exists.
Step budgets, token budgets, wall-clock timeouts and spend caps go in before the agent does anything interesting. An agent without limits is not a prototype, it is a bill.
Sending, paying, deleting, publishing. The approval checkpoint is a design pattern you implement in week one and apply to every project after it.
APIs time out, return nonsense and change shape. You learn to hand errors back to the model usefully, retry with backoff, and give up loudly rather than quietly.
Every run logs what was decided, which tool was called with what, and what came back. When a client asks why the agent did something at 3am, this is the difference between an answer and an apology.
This is also, in practice, the interview. Senior engineers ask about failure, not features.
Find your pace
The Agentic AI syllabus in the order you meet it, and what you are able to do by the end of each stretch of it. Every row is a capability, not a topic you sat through.
| Capability | Foundations | Applied | Professional |
|---|---|---|---|
| Agent loopsAgent Foundations | Covered in Foundations | Covered in Applied | Covered in Professional |
| State & contextAgent Foundations | Covered in Foundations | Covered in Applied | Covered in Professional |
| When not to use an agentAgent Foundations | Covered in Foundations | Covered in Applied | Covered in Professional |
| Cost modellingAgent Foundations | Covered in Foundations | Covered in Applied | Covered in Professional |
| Step budgetsAgent Foundations | Covered in Foundations | Covered in Applied | Covered in Professional |
| Tool schemasTool Use & Function Calling | Covered in Foundations | Covered in Applied | Covered in Professional |
| Parameter validationTool Use & Function Calling | Covered in Foundations | Covered in Applied | Covered in Professional |
| Error recoveryTool Use & Function Calling | Covered in Foundations | Covered in Applied | Covered in Professional |
| IdempotencyTool Use & Function Calling | Covered in Foundations | Covered in Applied | Covered in Professional |
| Retry policyTool Use & Function Calling | Covered in Foundations | Covered in Applied | Covered in Professional |
| Tool selectionTool Use & Function Calling | Covered in Foundations | Covered in Applied | Covered in Professional |
| Short & long-term memoryMemory, Planning & Retrieval | Not yet covered in Foundations | Covered in Applied | Covered in Professional |
| Task decompositionMemory, Planning & Retrieval | Not yet covered in Foundations | Covered in Applied | Covered in Professional |
| Planning strategiesMemory, Planning & Retrieval | Not yet covered in Foundations | Covered in Applied | Covered in Professional |
| RAG integrationMemory, Planning & Retrieval | Not yet covered in Foundations | Covered in Applied | Covered in Professional |
| Context compactionMemory, Planning & Retrieval | Not yet covered in Foundations | Covered in Applied | Covered in Professional |
| Orchestrator patternsMulti-Agent Systems & Safety | Not yet covered in Foundations | Not yet covered in Applied | Covered in Professional |
| Specialist agentsMulti-Agent Systems & Safety | Not yet covered in Foundations | Not yet covered in Applied | Covered in Professional |
| Critic & review loopsMulti-Agent Systems & Safety | Not yet covered in Foundations | Not yet covered in Applied | Covered in Professional |
| Human-in-the-loopMulti-Agent Systems & Safety | Not yet covered in Foundations | Not yet covered in Applied | Covered in Professional |
| Audit loggingMulti-Agent Systems & Safety | Not yet covered in Foundations | Not yet covered in Applied | Covered in Professional |
| DeploymentMulti-Agent Systems & Safety | Not yet covered in Foundations | Not yet covered in Applied | Covered in Professional |
The full programme runs 3 months. Where you finish is a question of pace rather than of syllabus — everyone covers all of it.
The case for it
Everything in this programme follows from that sentence. Approval steps, budgets, audit trails and recovery are not advanced topics here; they are the first week, because they are what makes an agent shippable.
You learn AI agents the way it is used in production, with the workflow, tooling and standards a working team expects. Nothing in the syllabus exists only to fill hours.
Sessions are hands-on. You do every example, break it, and fix it. Each module closes with a lab task that has to work before you move on.
Four shipped finished projects — the kind of work an interviewer can open, click through and question you on.
A structured final phase: portfolio review, profile cleanup, aptitude and role-specific practice, mock interviews and placement support.
Why this course
Six things we hold ourselves to for every Agentic AI batch that starts in Ludhiana.
Syllabus built around what AI agents teams actually ship — no filler modules, no theory that never reaches your hands.
Every concept lands in the tool the same day. You work in AI agents during each class, not just in your notes.
Build with real requirements, real data and real problems — from the first task to a finished, reviewable deliverable.
Trainers who work with AI agents daily review your work, your logic and your approach line by line.
Resume, portfolio, aptitude rounds and interview practice built into the last phase of the course.
Interview referrals, mock rounds and continued doubt support after the course ends.
Who can join
Six kinds of people sit in a typical batch, and none of them arrive knowing AI agents. Find the one that sounds like you.
You have finished school and want a skill that pays before a degree does. The first module assumes you have never opened AI agents in your life.
No background neededBCA, B.Tech, B.Sc, BBA, B.Com — any stream. You learn AI agents alongside your degree and finish with projects your syllabus was never going to give you.
Weekend batchesYou are months away from interviews and need work to show, not marks to quote. The final phase is portfolio, mock rounds and referrals.
Placement supportYou already have a job and want AI agents on top of it. Evening and weekend batches exist for exactly this, and the project work is yours to schedule.
Evening batchesYou are coming from a different field entirely — operations, teaching, accounts, sales. Every module starts from why, not from jargon.
Start from zeroYou want to do this work yourself instead of paying for it. You leave able to build, run and judge AI agents work on your own terms.
Practical onlyNot sure which of these you are, or whether the timing works around what you already do? That is exactly what the call is for. Ask about Agentic AI
Technology ecosystem
Agentic AI is the centre. These are the tools you use around it in a working team.
Hands-on projects
Each one lands in your portfolio with the working files, the process and something a reviewer can open.
Capstone project
A supervised multi-agent system that triages incoming requests, researches context, drafts a response and executes an action — with a critic agent reviewing every plan, a human approval gate on anything irreversible, bounded step and cost budgets, and a full audit log.
Certification
A techcadd Agentic AI certificate, an internship letter for the live-project phase, and deployed agents with their tool definitions, guardrails and run logs — the evidence a senior engineer will actually read.
Issued in your name on completion of the Agentic AI syllabus, with a reference number an employer can verify with us.
A separate certificate for the capstone you submit, naming the project so the work is attached to the credential.
Students who complete the live-project phase receive an internship letter covering the duration and the work delivered.
Every file, repository and deployed link stays yours — the part of the credential a reviewer can actually open.
techcadd has been training in Ludhiana since 2007. The certificate carries that record; the AI agents work you did carries the rest.
This is to certify that
has successfully completed the Agentic AI programme
Agentic AI Course
This is to certify that
for project work delivered under supervision in Ludhiana
Agentic AI capstone
Where it takes you
The route from your first module to the roles Agentic AI opens — and the work that has to exist at each step.
Design and ship autonomous systems for real workflows.
Build the agentic layer of an AI product.
Replace multi-step manual processes with supervised agents.
Run the infrastructure agents execute on, safely.
Scope where agents help a business and where they will not.
Own agentic features from prototype to production.
Salary outlook
Agentic work is the highest-paid corner of the AI stack and the smallest. There are few local openings with this exact title; the roles below are the ones this skill wins.
| Role | Ludhiana & Punjab | Delhi NCR & Bengaluru | Remote & freelance |
|---|---|---|---|
| AI Agent DeveloperEntry to mid | ₹4–7 LPA | ₹8–18 LPA | ₹60k–1.8L / project |
| Generative AI EngineerEntry to mid | ₹3.6–6.5 LPA | ₹7–16 LPA | ₹50k–1.4L / project |
| AI Automation EngineerEntry to mid | ₹3.5–6.5 LPA | ₹6.5–14 LPA | ₹45k–1.2L / project |
| AI Solutions ArchitectMid (3–5 yrs) | ₹8–14 LPA | ₹16–32 LPA | ₹2L–5L / project |
Ranges are indicative, drawn from what our own students report and from openings we see through the placement cell. Actual offers depend on your portfolio, the interview and the company — nobody can promise you a number, and we do not.
AI agent developer, generative AI engineer, AI automation engineer and, with a few years behind it, AI solutions architect. Job titles in this area are unsettled — most postings describe the work rather than name it, so search by what the role does.
Fast, from a high base, for a simple reason: very few engineers can demonstrate an agent that runs unsupervised without a horror story attached. Reliability work is the differentiator and it is what this course spends its time on.
Predominantly remote, and often international. Agent work is contracted globally and reviewed on artefacts — repository, run logs, evaluation — which suits an engineer in Ludhiana as well as one anywhere else.
Locally the demand comes from IT services firms, logistics and distribution operations around Ludhiana, larger manufacturing groups automating procurement and back-office work, and e-commerce sellers. Product companies in Mohali, Chandigarh, NCR and Bengaluru are the deeper market, and most of it is remote-friendly.
It is applied engineering rather than a research subject, but it is unusually strong evidence for a research-track application — an agent with a proper evaluation harness is close to a systems paper in miniature.
Future scope
A course ends; the field does not. Here is the honest version of what the next few years look like for AI agents — where the roles go, and what is shifting underneath them while you learn.
Entry roles such as AI Agent Developer open as soon as you have projects that run. At this stage nobody is asking about your marks — they are asking you to walk through something you built.
The generalists plateau; the specialists do not. Depth in one part of AI agents — the part your first job leans on hardest — is what moves you towards generative ai engineer work.
Architecture, standards, hiring and mentoring. The technical skill is assumed by now; what you are paid for is judgement, and judgement only comes from having shipped things that mattered.
Teams expect AI agents work to be done alongside AI tooling. It raises the floor on output and raises the bar on what counts as a junior — which is exactly why the fundamentals in this course are taught the hard way.
What used to be one narrow role now touches data, deployment and the product decision behind it. Every extra layer of AI agents you understand is another kind of room you get invited into.
Remote and hybrid hiring means Ludhiana, Jalandhar and Chandigarh candidates now compete for the same roles as Bengaluru ones. Location has stopped being the ceiling it used to be.
Hiring has moved to portfolios, take-home tasks and live problem solving. A certificate opens a shortlist; work that runs is what gets you through the round after it.
Sectors hiring for this skill set
The comparison
Every institute in Ludhiana claims industry training and placement support. These are the differences you can actually check on a demo visit — ask any of them of anyone, including us.
| What to ask about | techcadd | Typical institute |
|---|---|---|
| Who teaches | Trainers who still do AI agents work outside the classroom | Full-time faculty teaching from a fixed deck |
| 1-on-1 classes | One-to-one teaching available on every course — the pace is yours, and your work is looked at by name | One group class moving at one pace, whoever that pace happens to suit |
| Project work | 5 real projects plus a capstone you deploy and defend | Guided exercises copied from the board |
| Curriculum | Reviewed every batch against what working teams ship | Updated when the printed syllabus is reprinted |
| How a module ends | A lab task that has to run before you move on | Notes to revise before an exam |
| Doubt support | Trainer sits with your code; open lab hours between classes | Ask at the end of class if there is time left |
| What you leave with | Completion certificate, project certificate, internship letter — all verifiable | One printed certificate |
| After the course | Portfolio review, mock interviews, referrals, continued doubt support | The course ends and so does the contact |
Written about the market, not about any particular institute in it. Visit two or three, sit through a demo class at each, and ask all eight of these questions — that is the only version of this table worth trusting.
Student Voices
Feedback from students who completed the Agentic AI programme at techcadd Ludhiana.
Simranjeet Kaur
Agentic AI / B.Sc. IT graduate
I joined with no background in this. By the third month I was building AI agents work on my own, and the project reviews are where I actually learnt to do it properly.
Harman Sethi
Agentic AI / Now working as an intern in the field
The classes are practical. Every session ends with a task that has to work, so you cannot fake understanding. That habit helped me most in interviews.
Ankit Verma
Agentic AI / BCA final year
Doubt support was the difference for me. My trainer sat with my work, found the mistake and made me fix it myself instead of handing over the answer.
Navjot Singh
Agentic AI / Career switch from operations
I came for the skill and left with a portfolio — projects I could actually demo on a call, not a certificate I had to explain.
Simranjeet Kaur
Agentic AI / B.Sc. IT graduate
I joined with no background in this. By the third month I was building AI agents work on my own, and the project reviews are where I actually learnt to do it properly.
Harman Sethi
Agentic AI / Now working as an intern in the field
The classes are practical. Every session ends with a task that has to work, so you cannot fake understanding. That habit helped me most in interviews.
Ankit Verma
Agentic AI / BCA final year
Doubt support was the difference for me. My trainer sat with my work, found the mistake and made me fix it myself instead of handing over the answer.
Navjot Singh
Agentic AI / Career switch from operations
I came for the skill and left with a portfolio — projects I could actually demo on a call, not a certificate I had to explain.
Simranjeet Kaur
Agentic AI / B.Sc. IT graduate
I joined with no background in this. By the third month I was building AI agents work on my own, and the project reviews are where I actually learnt to do it properly.
Harman Sethi
Agentic AI / Now working as an intern in the field
The classes are practical. Every session ends with a task that has to work, so you cannot fake understanding. That habit helped me most in interviews.
Ankit Verma
Agentic AI / BCA final year
Doubt support was the difference for me. My trainer sat with my work, found the mistake and made me fix it myself instead of handing over the answer.
Navjot Singh
Agentic AI / Career switch from operations
I came for the skill and left with a portfolio — projects I could actually demo on a call, not a certificate I had to explain.
Real, unedited feedback from techcadd learners on Google.
Frequently asked questions
The 12 things people ask most often about the Agentic AI course at techcadd Ludhiana.
Python, and some experience calling an LLM API — a script that sends a prompt and handles the response is enough. If either is new, take the Generative AI course first. This is the one course where we will genuinely advise you to wait rather than enrol.
Generative AI is about getting a model to produce reliable output. Agentic AI is about letting it act — calling tools, taking multi-step decisions, changing real state. The second is the first plus consequences, which is why the safety, limits and approval work dominates the syllabus.
Claude with native tool use, LangGraph for orchestration, MCP for tool interfaces, plus FastAPI for serving and a logging setup you build yourself. Frameworks in this area turn over fast, so the emphasis is on the loop and the failure modes, which do not.
Few with that title today. Most students take this for remote roles, for product companies in Mohali, Chandigarh and NCR, or to be the person at a local IT firm who can build automation nobody else can. We would rather say that plainly than imply a local market that is not there yet.
2–3 months depending on batch, with the last phase given over to a live project. Because the prerequisite is real, batches are smaller here than in our other AI courses.
The complete Agentic AI programme runs for 3 months depending on the batch you choose. Weekday, weekend and fast-track options are available, along with shorter modules for students who only need the fundamentals.
School students after 12th, college students from any stream, graduates and working professionals changing track. The first module assumes no prior experience.
Python and some experience calling an LLM API. The Generative AI course is the natural prerequisite if you have neither.
Claude API, LangGraph, MCP, FAISS, Redis, FastAPI, Docker and Python — plus the day-to-day tooling and workflow that surrounds them in a real team.
Yes. Each module closes with a lab project, and the course ends with a capstone you can put on your portfolio and defend in an interview.
Placement support includes resume and portfolio review, aptitude and role-specific practice, mock interviews and interview referrals through our hiring network.
Yes. You receive a techcadd Agentic AI completion certificate, and a separate project certificate for the capstone you submit.
Next batch
Send us what you have already built with an LLM, however small, and we will tell you honestly whether to start here or with Generative AI first.
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