Flagship project
Python API Script
Write a Python script that calls an API and processes JSON data.
- Python
- Requests
- JSON
Multi-Agent AI
Multi-Agent AI Systems Real-World Applications Course in Amritsar with Live Labs & Placement Support
Multi-Agent AI Systems Real-World Applications Course course in AmritsarJob-oriented professional training built on live labs — small batches, daily hands-on practice and 100% placement assistance.
Flexible batchesBeginner to advancedHybrid
3 / 6 / 9 Months
Duration
Weekday/Weekend
Mode
12th Pass/Any Grad
Eligibility
10-12 Students
Batch Size
Admissions open
Multi-Agent AI Systems Real-World Applications Course · Flexible batches
Batch timings
- Morning
- Evening
- Weekend
- Live online
About the programme
Course overview
Course overviewtechcadd's Multi-Agent AI Systems Real-World Applications Course in Amritsar is a lab-based, professional-level programme for learners who want to work as AI engineers, multi-agent system developers, or AI application developers.
It opens with Python, LLM fundamentals, and prompt engineering, then moves into AI agents, multi-agent systems, orchestration, and AI application development.
You finish with a professional portfolio of deployed multi-agent AI applications, a capstone project, CV preparation and interview coaching.
Multi-agent AI is the practice of building systems where multiple autonomous AI agents collaborate, coordinate, and solve complex problems together. At the professional level, the job changes from writing single prompts to owning an agentic workflow — scoping the use case, designing agent roles and interactions, orchestrating collaboration, evaluating outputs, integrating AI into applications, and defending the results to a business. That is the layer this programme trains you for. Startups, IT firms, banks, healthcare providers, and manufacturers across Punjab are all adopting AI agents, and each of them needs engineers who can build, evaluate and explain a multi-agent AI system before it becomes a costly mistake.
Techcadd's Multi-Agent AI Systems Real-World Applications Course in Amritsar is built around the tools that appear in actual job descriptions — Python, OpenAI API, Gemini API, LangChain, AutoGen, CrewAI, and vector databases — not academic substitutes. You work in a hands-on lab from the first week and finish with a professional portfolio of deployed multi-agent AI applications, a capstone project, and interview-ready documentation. The programme is available in 3, 6, and 9-month tracks, and each longer track includes everything from the shorter one without repeating a module. Batch sizes are capped at 10–12 students, and every deliverable is reviewed weekly by mentors who still work on live AI engagements.
Python & APIsLLM FundamentalsPrompt DesignAI AgentsMulti-Agent Systems
- Duration
- Flexible batches
- Level
- Beginner to advanced
- Mode
- Hybrid
- Batches
- Morning, evening & weekend
Module by module
The curriculum, module by module
Module by module curriculumThe Multi-Agent AI Systems Real-World Applications Course is a nested ladder — each longer track includes every module from the shorter tracks and adds more depth. No module is repeated; you continue building on the skills you have already learned.
- Module 01 of 09
Python Fundamentals
Core Python concepts — syntax, variables, data types, operators, and control flow.
Topics covered
- Basics
- syntax
- variables
- operators
Tools in this module
PythonJupyter NotebookYou finish withA set of Python programs demonstrating core concepts
- Module 02 of 09
Python Data Structures & APIs
Python data structures and API calls — lists, tuples, sets, dictionaries, and working with JSON APIs.
Topics covered
- Lists
- tuples
- sets
- dictionaries
- API calls
Tools in this module
PythonRequestsJSONYou finish withA Python script that calls an API and processes JSON data
- Module 03 of 09
LLM Fundamentals
LLM fundamentals — tokens, context windows, model selection, and capabilities.
Topics covered
- LLMs
- tokens
- context windows
- model selection
Tools in this module
ChatGPTGeminiClaudeYou finish withA written comparison of LLMs and their use cases
- Module 04 of 09
Advanced AI Agents
Advanced AI agents — advanced agent patterns, tool use, planning, and agent-based workflows.
Topics covered
- Advanced agent patterns
- tool use
- planning
Tools in this module
LangChainAutoGenCrewAIYou finish withAn advanced AI agent with documented patterns and tools
- Module 05 of 09
Advanced Multi-Agent Systems
Advanced multi-agent systems — orchestration, collaboration, and agent-based workflows.
Topics covered
- Multi-agent systems
- orchestration
- collaboration
Tools in this module
LangChainAutoGenCrewAIYou finish withAn advanced multi-agent AI system with documentation
- Module 06 of 09
Vector Databases & Advanced RAG
Vector databases and advanced RAG — embeddings, similarity search, and integration with LLMs.
Topics covered
- Vector databases
- embeddings
- similarity search
Tools in this module
PineconeChromaDBWeaviateYou finish withA vector database-powered application
- Module 07 of 09
Advanced Multi-Agent AI Systems
Advanced multi-agent AI systems — orchestration, collaboration, and agent-based workflows.
Topics covered
- Advanced multi-agent systems
- orchestration
- collaboration
Tools in this module
LangChainAutoGenCrewAIYou finish withAn advanced multi-agent AI system with documentation
- Module 08 of 09
AI System Design
AI system design — architecture, scalability, and designing production AI systems.
Topics covered
- AI system design
- architecture
- scalability
Tools in this module
Architecture patternsAWSDockerYou finish withAn AI system design document with architecture
- Module 09 of 09
Cloud Deployment (AWS/GCP)
Cloud deployment for AI — AWS, GCP, Docker, and Kubernetes.
Topics covered
- Cloud deployment
- AWS
- GCP
Tools in this module
AWSGCPDockerKubernetesYou finish withA cloud-deployed multi-agent AI application with documentation
What you build
Projects that answer the interview question for you
Portfolio projectsEach project is reviewed individually, and the review notes go into your portfolio documentation.
Prompt Design Portfolio
Design a portfolio of prompts with evaluations.
- ChatGPT
- Gemini
- Claude
AI Agent
Build an AI agent with tool use and planning.
- LangChain
- AutoGen
- CrewAI
Multi-Agent System
Build a multi-agent AI system with LangChain or CrewAI.
- LangChain
- AutoGen
- CrewAI
AI Application
Deploy an AI application with FastAPI or Streamlit and Docker.
- FastAPI
- Streamlit
- Docker
RAG Pipeline
Build a retrieval-augmented generation pipeline with vector databases.
- LangChain
- Pinecone
- ChromaDB
The working loop
Learn it. Build it. Make it yours.
Learn it. Build it. Make it yours.Every project moves through the same loop: understand the brief, build it with guidance, then explain the decisions behind your work. The certificate is the receipt — the portfolio is the point.
- 01
Industry-focused curriculum
You learn it the way it is used on real AI projects, with the workflow and standards an AI team expects.
Python API Script
- 02
Live labs, not slides
Every module runs in a hands-on lab against real LLM APIs and agent frameworks — not screenshots.
Prompt Design Portfolio
- 03
Mentor-reviewed deliverables
Each module ends with a documented agent system and evaluation, reviewed weekly by a mentor who still works on live AI engagements.
AI Agent
- 04
Reporting and certification
Every exercise ends with a written report, and the programme closes with a practical assessment and certification.
Multi-Agent System
The honest pitch
Your months should build more than a certificate.
Your months should build more than a certificate.A course is worth the time you give it only if you finish with work you can show and skills you can defend.
Project completion certificate
Issued for the project you build and defend
Course completion certificate
Carries a certificate ID and a scan-to-verify code
Why choose Multi-Agent AI Systems Real-World Applications Course
Why students choose techcadd
Why students choose techcaddWhat makes this course at techcadd different from a typical alternative.
- 01
Mentors who still ship client work
Not full-time lecturers — trainers who are still doing the job they teach.
- 02
Live labs, not slides
Every module runs in a hands-on lab against real LLM APIs and agent frameworks — not screenshots.
- 03
Tools used in job descriptions
Python, OpenAI API, Gemini API, LangChain, AutoGen, CrewAI, Pinecone — not outdated or academic substitutes.
- 04
Nested ladder structure
Each longer track includes every module from the shorter one without repeating a module.
- 05
AI-assisted learning
You learn to use AI assistants and prompt engineering as practical tools in AI engineering.
Eligibility
Who can join the Multi-Agent AI Systems Real-World Applications Course course
Who can join the Multi-Agent AI Systems Real-World Applications Course courseThis course works for a specific starting point — here's who gets the most out of it.
- Educational backgroundNo specific degree or stream required — school leavers, graduates and career switchers all qualify.
- Prior experienceNone required. The programme opens at beginner level and builds from there across flexible batches.
- AgeNo upper age limit. What matters is being able to commit to the full course rather than dropping off midway.
- EquipmentA laptop or desktop with a stable internet connection for classroom or live-online batches.
- Time commitmentAble to attend regularly across flexible batches, in a morning, evening or weekend batch.
Where it leads
The roles this portfolio opens
Job titles vary by company, but the underlying expectations do not. Each destination below maps to work you will have already done during the programme.
Tools you will actually use
The working stack for Multi-Agent AI Systems Real-World Applications Course
Tools and technologies coveredEvery tool below is installed, configured and used by you during the course — not demonstrated on a slide. You leave able to set up your own environment from scratch.
20tools covered
Programming language
- Python
Development environment
- Jupyter Notebook
AI assistant
- ChatGPT
- Gemini
- Claude
AI API
- OpenAI API
- Claude API
- Gemini API
LLM framework
- LangChain
Multi-agent framework
- AutoGen
- CrewAI
Vector database
- Pinecone
- ChromaDB
- Weaviate
Web framework
- FastAPI
- Flask
Deployment framework
- Streamlit
Containers
- Docker
Orchestration
- Kubernetes
Cloud platform
- AWS
Certification
Get certified in Multi-Agent AI Systems Real-World Applications Course
Get certified in Multi-Agent AI Systems Real-World Applications CourseIssued on completion against the modules you finished and the projects you submitted — plus an internship letter where the industrial training track applies. Shareable to LinkedIn, and verifiable by an employer who calls the Amritsar desk.
- Module-wise assessment, not attendance-based
- Project submissions logged against your certificate
- Internship letter on the industrial training track
- Verifiable directly with the campus office
Future scope
Where this takes you over the next five years
Where this takes you over the next five yearsThe skill does not stand still, and neither does what it is worth.
- Multi-agent AI systemsAutonomous agents are moving from research to production applications.
- Generative AI and LLMsEvery application is adding AI features, making LLM skills increasingly valuable.
- RAG and vector databasesRetrieval-augmented generation is becoming standard for enterprise AI.
- Multi-modal AIModels that handle text, image, and audio are becoming standard.
- Responsible AIEthics, bias, and explainability are becoming standard requirements in AI job descriptions.
The objective is practical knowledge rather than memorised concepts.
Due diligence
How techcadd compares
How techcadd comparesWhat actually differs between this course at techcadd and a typical alternative.
| What you should ask | techcadd Amritsar | Other institutes |
|---|---|---|
| Live multi-agent AI project work | Every course, from week one | Varies, often only at the end |
| Small batches (10–12) | Capped for mentor attention | Often 30+ students per batch |
| Tools used in job descriptions | Python, OpenAI API, Gemini API, LangChain, AutoGen, CrewAI, Pinecone | Outdated or academic substitutes |
| Mentor-reviewed deliverables | Weekly review by working professionals | Often no review or feedback |
| AI-assisted learning | Built into the curriculum | Rarely included |
Every comparison here is about substance, not marketing language.
Before you enrol
Questions we get asked at the admissions desk
Frequently asked questionsIf something here is not covered, the Amritsar desk will answer it directly — no call-back queue.
No. The programme opens at beginner level and assumes no background beyond comfort with a computer. Students who already have some exposure move through the early modules faster and spend the saved time on project work.
