Flagship project
Research & Report Crew
Build a multi-agent crew with a Researcher, Writer, and Editor agent that collaborates to produce a well-structured report on a given topic.
- CrewAI
- Collaboration
- Automation
Multi-Agent AI SystemsAdvanced Skill
Multi-Agent AI Systems Course in Amritsar – Build Collaborative AI from Foundation to Advanced
Multi-Agent AI Systems: Foundation to Advanced Course course in AmritsarA hands-on, advanced training in Amritsar to master multi-agent AI. Learn agentic design, orchestration with LangChain & CrewAI, and build a portfolio of collaborative AI projects with placement support.
6 / 12 WeeksIntermediateHybrid
6 / 12 Weeks
Duration
Weekday/Weekend
Mode
12th Pass/Any Grad (Basic Python knowledge recommended)
Eligibility
8-10 Students
Batch Size
Admissions open
Multi-Agent AI Systems: Foundation to Advanced Course · 6 / 12 Weeks
Batch timings
- Morning
- Evening
- Weekend
- Live online
About the programme
Course overview
Course overviewtechcadd's Multi-Agent AI Systems course in Amritsar is a deep, project-driven program for developers, data scientists, and AI enthusiasts ready to move beyond single-model interactions. It bridges the gap between knowing about LLMs and building sophisticated, autonomous systems with them.
It begins with the core principles of agentic design, agent roles, and communication protocols. You then rapidly progress to hands-on development using industry-standard frameworks like LangChain, CrewAI, and AutoGen, applying them to real-world use cases in research, content creation, and process automation.
You finish the course with a portfolio of working multi-agent systems, a capstone project, and the confidence to architect and lead the development of collaborative AI solutions in any organization.
The next wave of AI isn't just about a single, powerful model answering a question. It's about creating teams of specialized AI agents that collaborate, debate, and work together to solve problems far too complex for any single AI. This is the world of Multi-Agent AI Systems, and it's the frontier where the most innovative companies are building the future.
While a single LLM can write a poem or answer a query, a multi-agent system can research a topic, write a report, design a slide deck, and critique its own work—all autonomously. This shift from a single "AI co-pilot" to an entire "AI crew" is revolutionizing industries, from automated software development to dynamic market analysis and advanced scientific research. The ability to architect, build, and manage these collaborative AI systems is set to become one of the most sought-after and highly valued skills in the tech industry.
techcadd's Multi-Agent AI Systems: Foundation to Advanced Course in Amritsar is your gateway to this revolutionary field. This program is meticulously designed to take you from the fundamentals of agentic design to the advanced techniques of orchestration and deployment. You won't just learn the theory; you'll get your hands dirty with frameworks like LangChain, CrewAI, and AutoGen from the very first week. You'll learn how to design agent roles, enable communication, manage complex workflows, and build a portfolio of sophisticated AI systems that solve real-world problems. Batches are intentionally small, ensuring you receive dedicated mentorship and direct feedback as you build the skills to lead in the era of collaborative AI.
Placement assistanceLive AI labsInternship certificateCapstone Project
- Duration
- 6 / 12 Weeks
- Level
- Intermediate
- Mode
- Hybrid
- Batches
- Morning, evening & weekend
Module by module
The curriculum, module by module
Module by module curriculumThe Professional Track is a nested ladder — it includes every module from the 6-week Foundation track and adds more depth. No module is repeated; you continue building on the skills you have already learned.
- Module 01 of 04
Agentic AI & LLM Orchestration Fundamentals
The foundational knowledge for building intelligent agents. You'll learn how to break down complex problems and use LLMs to create reasoning chains and simple, stateful agents.
Topics covered
- Agentic design principles
- agent roles
- task decomposition
- LLM as a reasoning engine
- prompt chaining
- sequential & hierarchical workflows
- introduction to LangChain
Tools in this module
PythonOpenAI APIAnthropic APILangChainGoogle AI StudioYou finish withA simple, single-agent system that can perform a multi-step task, like generating a summary of a webpage after finding it via a search tool
- Module 02 of 04
Multi-Agent Systems & Frameworks
Moving from single agents to collaborative teams. You'll learn how to design agent crews, assign them specialized roles, and have them work together to achieve a common goal.
Topics covered
- Multi-agent architectures
- agent communication
- collaborative task-solving
- introduction to CrewAI and AutoGen
- defining agent roles and goals
Tools in this module
CrewAIAutoGenLangChainGitHubYou finish withA multi-agent "crew" that can collaboratively research a topic and produce a structured article or report
- Module 03 of 04
Advanced Agentic Patterns & Tool Use
Making your agents more powerful and reliable. You'll learn how to give agents long-term memory, teach them to use external tools and APIs, and build complex, stateful graphs of agents.
Topics covered
- Agent memory
- tool use and function calling
- RAG for agents
- advanced orchestration
- error handling and self-correction
- agent-based simulation
Tools in this module
PineconeChromaDBLangGraphLlamaIndexYou finish withA RAG-powered agent that can answer questions from a custom knowledge base and use tools to interact with the outside world
- Module 04 of 04
Autonomous Agents & Deployment
Taking your systems from a local script to a deployed, usable application. This module focuses on building robust, autonomous systems and preparing them for real-world use.
Topics covered
- Autonomous task execution
- multi-agent simulations
- human-in-the-loop workflows
- deployment strategies
- monitoring and evaluation of AI systems
- capstone project development
Tools in this module
StreamlitGradioDockerAWSGCPYou finish withA fully functional, deployed multi-agent system that solves a complex problem from your capstone project
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.
RAG-Powered Customer Support Agent
Create an agent that uses RAG to answer customer questions from a company's knowledge base and can use tools to escalate issues or book appointments.
- RAG
- Agent
- Tool Use
- LangChain
Autonomous Code Generation System
Develop a system of agents that can take a simple feature request, write the code, generate unit tests, and debug its own work.
- AutoGen
- Autonomous
- Code Generation
Capstone Project
Architect and deploy an end-to-end multi-agent system that solves a complex business problem, from ideation to a live, interactive demo.
- Multi-Domain
- Portfolio
- Deployment
- Architecture
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
Advanced, Future-Proof Curriculum
You learn the frameworks and architectural patterns that are defining the future of AI, moving beyond basic API calls to true system design.
Research & Report Crew
- 02
Hands-On, Framework-First Learning
Every session is a lab. You work directly with LangChain, CrewAI, and AutoGen to build, test, and debug collaborative agent systems.
RAG-Powered Customer Support Agent
- 03
Mentor-Reviewed Capstone Project
You will architect and build a complete multi-agent system, reviewed by mentors who build these systems professionally, ensuring your work is industry-relevant.
Autonomous Code Generation System
- 04
Build an Advanced Portfolio
You finish the course with a portfolio of documented multi-agent projects, providing tangible proof of your advanced skills to employers or clients.
Capstone Project
The honest pitch
Your skills should build the systems of tomorrow, today.
Your skills should build the systems of tomorrow, today.A course is worth your time only if it equips you to build the next generation of AI applications, not just use the last one.
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: Foundation to Advanced Course
Why students choose techcadd
Why students choose techcaddWhat makes this course at techcadd different is that it is built for the future, focusing on the advanced skills needed to architect and build collaborative AI systems, not just use them.
- 01
Mentors who build advanced AI systems
Learn from trainers who are actively architecting and deploying multi-agent systems in the industry.
- 02
One complex project, built from start to finish
You start your capstone project in Module 01 and complete it by the final module, guided by mentors.
- 03
Ultra-small batch sizes
8–10 students per batch ensures every student gets personalised, in-depth feedback on their architectural designs and code.
- 04
Nested, no-repeat curriculum
The 12-week program includes everything from the 6-week foundation course and adds more depth — you never repeat a module.
- 05
Documented internship experience
Every student receives a documented internship certificate alongside their course certificate.
- 06
Placement support
Resume and LinkedIn preparation, interview coaching, and active placement assistance for graduates.
Eligibility
Who can join the Multi-Agent AI Systems: Foundation to Advanced Course course
Who can join the Multi-Agent AI Systems: Foundation to Advanced Course courseThis advanced course is built for those ready to lead the next wave of AI. Here's who will benefit most.
- Educational backgroundNo specific degree or stream required — school leavers, graduates and career switchers all qualify.
- Prior experienceNone required. The programme opens at intermediate level and builds from there across 6 / 12 weeks.
- 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 6 / 12 weeks, 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: Foundation to Advanced 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.
19tools covered
Programming language
- Python
LLM framework
- LangChain
- LlamaIndex
Multi-agent framework
- CrewAI
- AutoGen
Agent orchestration
- LangGraph
LLM API
- OpenAI API
- Anthropic API
Vector database
- Pinecone
- ChromaDB
AI community
- Hugging Face
App framework
- Streamlit
- Gradio
Containerization
- Docker
Version control
- Git
Code hosting
- GitHub
Code editor
- VS Code
Notebook environment
- Jupyter
Cloud notebooks
- Google Colab
Certification
Get certified in Multi-Agent AI Systems: Foundation to Advanced Course
Get certified in Multi-Agent AI Systems: Foundation to Advanced 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 of building collaborative AI is not just a trend; it's the architectural foundation of future software.
- From single-agent to multi-agentThe industry is rapidly moving from simple co-pilots to autonomous crews that can handle end-to-end processes.
- Orchestration is the new programmingThe ability to design, orchestrate, and manage collaborative AI systems will be as fundamental as object-oriented programming was in the past.
The objective is practical, architectural knowledge that you can use to build the future of AI.
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 project work | Every module, from week one | Varies, often only at the end |
| Learning format | One complex project extended through every module | Topics demonstrated separately, rarely combined |
| Tools used | LangChain, CrewAI, AutoGen, Python | Usually limited to basic ChatGPT |
| Batch size | 8–10 students per batch | Often 25–40 students per batch |
| RAG & AI Agents | Live integration, real projects | Rarely covered |
| Advanced topics | Fine-tuning, MLOps, Automation | Often only theory |
| Automation & Scripts | AI pipelines, Zapier, Make | Rarely covered |
| Portfolio | 6 documented project deliverables | Often just assignments, not portfolio-ready work |
| Certificate | Completion certificate + documented internship | Only a completion certificate |
| Placement support | Resume, LinkedIn, interview coaching, active placement | Often only a completion certificate |
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 intermediate 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.
