🤖 AI Project #12: Multi-Agent AI System

A Multi-Agent AI System consists of multiple specialized AI agents working together to solve complex tasks. Instead of one AI handling everything, different agents collaborate, each with a specific responsibility.

This is the type of architecture used in many enterprise AI applications.

🎯 Project Goal

Build a Multi-Agent AI System where different AI agents work together to complete a task from start to finish.

Example workflow:

User Request

↓

Planner Agent

↓

Research Agent

↓

Coding Agent

↓

Reviewer Agent

↓

Report Generator

↓

Final Response

🧠 Skills You'll Learn

Generative AI: Multi-Agent Systems, Agent Orchestration, Tool Calling, Prompt Engineering

Frameworks: LangGraph, LangChain, CrewAI, AutoGen

Backend: FastAPI, Python, REST APIs

Databases: Vector Databases, SQL Databases, Memory Stores

📌 Why Multi-Agent Systems?

Instead of:

❌ One AI trying to do everything

Use:

✅ Specialized AI agents that collaborate

Benefits:

Better accuracy, Modular design, Easier debugging, Scalable architecture, Parallel execution

🏗️ System Architecture

User

│

▼

Planner Agent

┌──────┴──────┐

▼ ▼

Research Agent Coding Agent

│ │

└──────┬──────┘

▼

Reviewer Agent

│

▼

Report Generator

│

▼

Final Response

📂 Step 1: Install Libraries

pip install langgraph

pip install langchain

pip install crewai

pip install openai

pip install streamlit

🤖 Step 2: Define AI Agents

Planner Agent

Responsibilities: Understand user goal, Break task into subtasks, Assign work

Research Agent

Responsibilities: Collect information, Search documentation, Summarize findings

Coding Agent

Responsibilities: Generate code, Improve code, Debug code

Reviewer Agent

Responsibilities: Check quality, Detect errors, Suggest improvements

Report Agent

Responsibilities: Combine outputs, Create final report, Generate summary

📝 Step 3: Create Agent Prompts

Planner Prompt: Break the user's request into smaller tasks.

Research Prompt: Find accurate information from the provided sources.

Coding Prompt: Write production-ready Python code with comments.

Reviewer Prompt: Review the solution, identify issues, and suggest improvements.

🔄 Step 4: Build the Workflow

Example Flow:

User: Build a spam email detector.

Planner: 1. Understand requirements 2. Identify technologies 3. Assign research

↓

Research Agent: Collect ML algorithms, Dataset suggestions, Evaluation metrics

↓

Coding Agent: Generate project code

↓

Reviewer: Improve efficiency, Fix bugs

↓

Report Agent: Create README, Deployment steps, Project summary

🛠️ Step 5: Add External Tools

Agents can use tools such as: Web search, Calculator, Python execution, SQL database, Vector database, File system, Email APIs

Example:

tool = search_tool()

result = tool.invoke("Latest AI news")

🧠 Step 6: Add Shared Memory

Instead of each agent working independently, they share context.

Example:

Planner: Build AI chatbot.