Modern AI applications are no longer single, monolithic chatbots. As use cases expand across technology, healthcare, finance, travel, security, and operations, a single general-purpose model quickly becomes inefficient, expensive, and hard to control.
This is where multi-agent AI systems come in.
A multi-agent system divides responsibility across specialized AI agents, each designed to handle a specific domain. Instead of asking one model to “know everything,” the system intelligently decides:
- Which agent is best suited for this question?
- Which model should respond?
- How do we preserve conversation context across agents?
In this tutorial, we implement this architecture using:
- AWS Agent Squad for orchestration
- Amazon Bedrock for foundation models
- Streamlit for a clean, interactive UI
The result is a scalable, cost-efficient, and production-friendly AI assistant.
🧠 What Is AWS Agent Squad?
Agent Squad is an open-source orchestration framework from AWS that helps you build collaborative, multi-agent AI systems.
At a high level, Agent Squad provides:
- A central orchestrator to manage agents
- Automatic intent-based routing
- Shared conversation memory
- Clean abstractions for adding new agents or tools
You define:
- What each agent specializes in
- Which foundation model it uses
- How requests are routed
Agent Squad handles the rest.
🧩 Core Concepts You Should Understand
Before diving deeper, it’s important to understand the three core building blocks.
1️⃣ Orchestrator (The Brain)
The AgentSquad orchestrator is the control plane of the system.
Its responsibilities include:
- Receiving user input
- Maintaining session and conversation context
- Comparing the query against agent descriptions
- Selecting the most relevant agent
- Forwarding the request and returning the response
Think of it as an AI traffic controller.
2️⃣ Specialized Agents (Domain Experts)
Each agent is configured with:
- A name
- A clear domain description
- A foundation model from Amazon Bedrock
Examples:
- Tech Agent → AWS, cloud, software engineering
- Health Agent → fitness, wellness, nutrition
- Travel Agent → destinations, itineraries, tourism
The descriptions are critical—they are what the classifier uses to decide who should answer.
3️⃣ Classifier Model (The Router)
Agent Squad uses a lightweight classifier model (Claude Haiku in this tutorial) to analyze:
- The user’s question
- All agent descriptions
- Previous conversation context
It does not generate the final answer.
Its only job is to determine which agent is best suited.
This keeps costs low and routing fast.
🔄 How the System Works (End-to-End Flow)
Let’s walk through a complete request lifecycle.
Step 1: User Submits a Query
The user types a question into the Streamlit chat UI, such as:
“What is AWS Lambda?”
Step 2: Request Reaches the Orchestrator
The Streamlit frontend sends the input to the Agent Squad orchestrator, along with:
- User ID
- Session ID
- Conversation history
Step 3: Intelligent Agent Selection
The orchestrator invokes the classifier model (Claude Haiku), providing:
- The user query
- Agent descriptions
- Context from prior messages
The classifier evaluates semantic relevance and selects the Tech Agent.
Step 4: Response Generation
The selected agent (using Claude Sonnet) generates a high-quality, domain-specific response.
Because the agent is specialized:
- Answers are more accurate
- Prompts are shorter
- Token usage is optimized
Step 5: Response Returned to the UI
The response is:
- Displayed to the user
- Tagged with the agent name
- Stored in session history for context continuity
Demo:
💡 Why This Architecture Is Powerful
This pattern offers several key advantages:
✅ Specialization Over Generalization
Each agent is optimized for a domain, improving accuracy and relevance.
✅ Lower Costs
Only the selected agent generates the response.
The classifier uses a cheaper, faster model.
✅ Easy Extensibility
Adding a new domain is as simple as registering another agent.
✅ Production Readiness
Clear separation of concerns:
- UI
- Routing
- Reasoning
- Model invocation
✅ Enterprise-Friendly
This architecture aligns well with:
- Security boundaries
- Compliance requirements
- Observability and monitoring
🏭 Real-World Use Cases
This multi-agent pattern is ideal for:
- Customer support platforms
- Internal developer portals
- AI-powered knowledge bases
- Multi-domain enterprise assistants
- Workflow automation systems
- DevOps and cloud operations copilots
Reference:
Github: https://github.com/awslabs/agent-squad
Happy Learning
Prithiviraj Rengarajan
DevOps Engineer
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