Module 6 Hands-On Lab: Multi-Agent Campus Assistant
Lab Overview
Congratulations!
You have completed Module 6: Multi-Agent Systems.
So far, you have learned:
Agent-to-Agent Communication
Agent Orchestration
Supervisor Agents
Research Agents
Coding Agents
Customer Support Agents
You now understand that modern AI systems are no longer built around a single agent.
Instead, organizations increasingly build:
Agent Teams
Agent Networks
Agent Workflows
Multi-Agent Platforms
In this hands-on project, we will combine everything learned in Module 6 to design a complete Multi-Agent Campus Assistant.
This project closely resembles the type of systems universities, enterprises, and government organizations are beginning to adopt.
Learning Objectives
By completing this lab, you will:
Design a real-world multi-agent architecture.
Apply agent communication patterns.
Implement orchestration concepts.
Design supervisor-driven workflows.
Integrate MCP resources.
Build enterprise-scale agent systems.
Strengthen architecture and interview skills.
Project Scenario
A university wants to create an AI-powered Campus Assistant capable of helping students, faculty members, and administrative staff.
The assistant should support:
Admissions
Academics
Placements
Scholarships
Research
Technical Support
Career Guidance
The university wants a single AI platform that feels like a digital campus companion.
Understanding the Problem
A single AI agent may struggle because:
Multiple departments exist.
Different expertise is required.
Different data sources are involved.
Different workflows must be managed.
This makes a multi-agent architecture the ideal solution.
High-Level Architecture
Students
Faculty
Staff
?
Campus Assistant
?
Supervisor Agent
?
Specialized Agents
?
University Systems
The Supervisor Agent becomes the central coordinator.
Step 1: Define Specialized Agents
The first step is identifying the required agents.
Admission Agent
Responsibilities:
Admission Eligibility
Required Documents
Admission Deadlines
Application Process
Example Question:
How do I apply for MCA admission?
Academic Agent
Responsibilities:
Course Information
Attendance
Academic Regulations
Examination Rules
Example Question:
What is the minimum attendance requirement?
Placement Agent
Responsibilities:
Placement Readiness
Skill Assessments
Mock Interviews
Placement Roadmaps
Example Question:
Am I ready for campus placements?
Scholarship Agent
Responsibilities:
Scholarship Discovery
Eligibility Verification
Application Guidance
Example Question:
Which scholarships can I apply for?
Career Agent
Responsibilities:
Career Planning
Skill Recommendations
Industry Trends
Certification Guidance
Example Question:
How do I become an AI Engineer?
Research Agent
Responsibilities:
Research Support
Literature Reviews
Trend Analysis
Paper Summarization
Example Question:
Summarize recent AI Agent research.
Technical Support Agent
Responsibilities:
Portal Issues
Account Problems
System Access
Password Resets
Example Question:
I cannot access the student portal.
Coding Agent
Responsibilities:
Programming Guidance
Project Suggestions
Code Reviews
Debugging Help
Example Question:
Help me build an ASP.NET Core API.
Multi-Agent Architecture
Supervisor Agent
?
Admission Agent
Academic Agent
Placement Agent
Scholarship Agent
Career Agent
Research Agent
Technical Support Agent
Coding Agent
Each agent specializes in a specific domain.
Step 2: Add MCP Infrastructure
Agents need access to enterprise data.
Possible MCP Servers:
Student Records MCP Server
Placement MCP Server
Scholarship MCP Server
Research MCP Server
Document MCP Server
Technical Support MCP Server
These servers expose resources and tools.
MCP Architecture
Agents
?
MCP Layer
?
University Systems
The MCP layer standardizes access.
Step 3: Add Knowledge Sources
The Campus Assistant should use:
Student Handbook
Admission Policies
Placement Guidelines
Scholarship Rules
Academic Regulations
Research Documentation
These documents become searchable knowledge resources.
Step 4: Add Shared Memory
Shared memory enables collaboration.
Examples:
Student Goals
Skill Progress
Placement Status
Learning History
All agents can access this information.
Why Shared Memory Matters
Without memory:
Agents repeatedly ask the same questions.
With memory:
Recommendations become personalized.
This improves user experience significantly.
Step 5: Implement Agent Communication
Agents should communicate when required.
Example:
Student asks:
How do I become an AI Engineer?
Workflow:
Career Agent
?
Placement Agent
?
Coding Agent
?
Response
Each agent contributes expertise.
Step 6: Add Orchestration
The Supervisor Agent manages workflows.
Responsibilities:
Task Assignment
Agent Selection
Progress Tracking
Result Aggregation
This creates structured collaboration.
Example Workflow
Student asks:
Create a placement preparation plan.
Supervisor assigns:
Career Agent
Placement Agent
Coding Agent
Results are combined into a roadmap.
Step 7: Add Human-in-the-Loop
Certain actions require human approval.
Examples:
Scholarship Approval
Admission Decisions
Academic Exceptions
Official Placement Reports
Workflow:
Agent Recommendation
?
Human Review
?
Final Decision
This improves governance.
Student Journey Example
Student:
I want to become an AI Engineer.
Workflow:
Career Agent
Analyzes career goals.
Placement Agent
Evaluates readiness.
Coding Agent
Suggests projects.
Research Agent
Provides industry trends.
Supervisor Agent
Combines results.
Final output:
A personalized career roadmap.
Placement Preparation Workflow
Student asks:
Am I placement-ready?
Workflow:
Supervisor Agent
?
Placement Agent
Coding Agent
Career Agent
?
Assessment
?
Response
This creates comprehensive guidance.
Scholarship Workflow
Student asks:
Which scholarships am I eligible for?
Workflow:
Scholarship Agent
?
Scholarship MCP Server
?
Eligibility Tool
?
Recommendations
The process becomes automated.
Research Workflow
Faculty member asks:
Analyze recent developments in AI Agents.
Workflow:
Research Agent
?
Knowledge Sources
?
Analysis
?
Summary Report
Research productivity improves significantly.
Technical Support Workflow
Student asks:
I forgot my password.
Workflow:
Technical Support Agent
?
Identity Verification
?
Password Reset Tool
?
Resolution
Support becomes faster.
Enterprise Architecture
Recommended architecture:
React Frontend
?
ASP.NET Core API
?
Supervisor Agent
?
Specialized Agents
?
Shared Memory
?
MCP Servers
?
University Systems
This architecture closely resembles real-world deployments.
Mapping Technologies
Frontend
React
Backend
ASP.NET Core
Agent Framework
OpenAI Agents SDK
or
Semantic Kernel
Database
SQL Server
Vector Database
For RAG and knowledge retrieval
MCP Layer
Enterprise MCP Servers
This stack is highly practical.
Security Considerations
Enterprise systems must include:
Authentication
Authorization
Audit Logs
Data Privacy
Role-Based Access
Security should be built from the beginning.
Scalability Considerations
As the university grows:
More Students
More Faculty
More Agents
More Data
The architecture should scale horizontally.
This is why modular agent architectures are preferred.
Common Challenges
Challenge 1
Agent Coordination
Challenge 2
Knowledge Consistency
Challenge 3
Shared Memory Management
Challenge 4
Tool Reliability
Challenge 5
Workflow Complexity
Understanding these challenges is important for production systems.
Career Perspective
This project demonstrates many highly valuable skills:
Agent Engineering
Multi-Agent Systems
MCP Integration
RAG Architecture
Enterprise AI Design
These skills are increasingly requested by employers.
Module Assessment
Question 1
Which component coordinates all agents?
A. Placement Agent
B. Coding Agent
C. Supervisor Agent
D. MCP Server
Answer:
C
Question 2
Which component provides access to enterprise resources?
A. Shared Memory
B. MCP Server
C. Career Agent
D. Research Agent
Answer:
B
Question 3
Which pattern improves collaboration between agents?
A. Shared Memory
B. Hardcoded Logic
C. Static Prompts
D. Manual Routing
Answer:
A
Question 4
Which agent would analyze research papers?
A. Placement Agent
B. Coding Agent
C. Research Agent
D. Admission Agent
Answer:
C
Question 5
What is the primary role of a Supervisor Agent?
A. Store Data
B. Manage Databases
C. Coordinate Agents
D. Generate Embeddings
Answer:
C
Key Takeaways
Modern AI systems increasingly use multiple specialized agents.
Supervisor Agents coordinate workflows.
MCP provides access to enterprise resources.
Shared memory enables personalization.
Multi-agent architectures improve scalability.
Human oversight remains important.
Enterprise AI systems combine agents, memory, tools, and governance.
Module 6 Summary
You now understand:
Agent Communication
Agent Orchestration
Supervisor Agents
Research Agents
Coding Agents
Customer Support Agents
You have learned how multiple AI agents collaborate to solve complex problems and build enterprise-grade AI systems.
This knowledge forms the foundation for building production-ready agent ecosystems.
What's Next?
In Module 7, we move from building agents to operating agents in production environments.