Multi-Agent Orchestration

Multi-agent orchestration enables sophisticated AI systems where specialized agents work together, each focusing on their area of expertise. Instead of one generalist agent handling everything, you create teams of specialist agents that collaborate to solve complex problems.

What It Does

Multi-Agent Orchestration allows you to define multiple agents within a single chat app, where a supervisor agent coordinates the work of specialist agents. Each agent has its own tools, knowledge, and instructions, enabling division of labor for complex tasks.

Why It Matters

Complex tasks often require different types of expertise. Rather than building one massive agent that tries to do everything, multi-agent systems let you:

• Separate concerns - Each agent focuses on what it does best
• Improve quality - Specialists outperform generalists
• Enhance maintainability - Update agents independently
• Enable scaling - Add new specialists without affecting existing ones
• Provide transparency - See which agent handled which part of the task

How It Works

Supervisor-Specialist Pattern

The most common pattern uses a supervisor agent that:

1. Understands the user request
2. Identifies required specialists
3. Delegates to appropriate agents
4. Synthesizes their responses
5. Returns coherent answer to user

Agent Collaboration

Agents collaborate through structured workflows:

• Supervisor analyzes the request
• Routes specific sub-tasks to specialists
• Specialists execute with their tools and knowledge
• Supervisor integrates specialist outputs
• User sees seamless, comprehensive response

Use Cases

Customer Support with Escalation

Structure:

• Tier 1 Agent - Handles common questions using knowledge base
• Billing Specialist - Resolves payment and invoice issues
• Technical Specialist - Troubleshoots product problems
• Escalation Agent - Creates tickets for human agents

Workflow: User asks about billing issue → Supervisor routes to Billing Specialist → Specialist accesses payment system → Returns solution

Financial Analysis System

Structure:

• Query Specialist - Understands financial questions
• Data Retrieval Agent - Accesses databases and APIs
• Analysis Agent - Performs calculations and trend analysis
• Visualization Agent - Creates charts and reports

Workflow: User requests revenue analysis → Query Specialist interprets → Data Retrieval fetches records → Analysis calculates metrics → Visualization creates charts

Enterprise Knowledge Management

Structure:

• Router Agent - Classifies questions by domain
• HR Policy Agent - Answers HR-related questions
• IT Help Desk Agent - Resolves technical issues
• Finance Agent - Handles expense and budget questions

Workflow: Employee asks question → Router classifies domain → Specialist provides domain-specific answer using appropriate tools

Development Assistant

Structure:

• Code Review Agent - Analyzes code quality
• Documentation Agent - Explains code and generates docs
• Testing Agent - Suggests test cases
• Security Agent - Identifies vulnerabilities

Workflow: Developer submits code → Each specialist reviews their domain → Supervisor synthesizes comprehensive feedback

Key Benefits

Expertise Specialization

Each agent focuses on a specific domain with appropriate tools and knowledge, delivering higher quality results than a generalist approach.

Maintainability

Update specialist agents independently without affecting the entire system. Add, remove, or modify specialists as needs evolve.

Transparency

See exactly which agent handled which part of the task. Debug and refine agents individually based on performance.

Scalability

Add new specialists to handle additional domains without rewriting existing agents. Scale complexity by adding agents, not complexity.

Configuration Example

Define multiple agents in your chat app:

const chatAppConfig: ChatAppConfig = {
    chatAppId: 'customer-support',
    chatAppName: 'Customer Support',
    agentIds: [
        'supervisor-agent',      // Coordinates specialists
        'billing-specialist',    // Handles billing
        'technical-specialist',  // Handles technical issues
        'order-specialist'       // Handles orders
    ],
    // Supervisor agent has access to specialist agents as "tools"
    defaultAgentId: 'supervisor-agent'
};

The supervisor agent configuration includes references to specialist agents:

const supervisorConfig: AgentConfig = {
    agentId: 'supervisor-agent',
    agentName: 'Customer Support Supervisor',
    instructions: 'Analyze customer requests and route to appropriate specialists...',
    // Specialist agents appear as tools the supervisor can invoke
    toolIds: [
        'billing-specialist-tool',
        'technical-specialist-tool',
        'order-specialist-tool'
    ]
};

Design Patterns

Hub-and-Spoke

One supervisor coordinates multiple specialists:

User → Supervisor → Specialist A
                  → Specialist B
                  → Specialist C

Best for: Clear domain boundaries, independent sub-tasks

Sequential Pipeline

Agents process in sequence:

User → Agent A → Agent B → Agent C → Result

Best for: Multi-stage workflows, each building on previous output

Hierarchical Teams

Supervisors manage sub-supervisors:

User → Main Supervisor → Team A Supervisor → Specialist A1
                                            → Specialist A2
                       → Team B Supervisor → Specialist B1
                                            → Specialist B2

Best for: Large-scale systems, complex organizational structures

Best Practices

Keep Agents Focused

Each agent should have a clear, narrow responsibility:

• Good: "Handle billing disputes under $1000"
• Bad: "Handle all customer issues"

Define Clear Interfaces

Supervisor and specialists communicate through well-defined interfaces:

• Explicit input schemas
• Structured output formats
• Error handling protocols
• Success/failure signals

Implement Fallback Strategies

Handle cases where specialists can't solve the problem:

• Escalation to human agents
• Alternative specialist routing
• Graceful degradation
• Clear error messages to users

Monitor Agent Performance

Track metrics for each agent:

• Success rates
• Response times
• User satisfaction
• Error frequencies

Advanced Features

Dynamic Agent Selection

Supervisors intelligently choose specialists based on:

• User query analysis
• Current system state
• Specialist availability
• Historical performance

Agent Learning

Agents improve over time through:

• User memory capturing preferences
• Feedback on agent performance
• Instruction refinement
• Tool optimization

Cross-Agent Context

Share context between agents:

• User preferences available to all
• Session history accessible
• Previous specialist outputs visible
• Consistent entity context

Performance Considerations

Multi-agent systems add complexity but deliver better results:

• Latency: Slightly higher due to routing, but specialists often faster
• Cost: More LLM calls but smaller context windows per agent
• Quality: Significantly better for complex, multi-domain tasks
• Debugging: Easier to identify and fix specific agent issues

Getting Started

Review Weather Sample

See a simple multi-agent system in action.

Explore Sample →

Learn the Concepts

Understand agent architecture and tool systems.

Read Concepts →

Implementation Guide

Step-by-step instructions for building multi-agent systems.

How-To Guide →

Related Capabilities

Agents as Config

Define agents declaratively for easy management and updates.

Learn More →

Direct Agent Invocation

Call individual agents programmatically for system integrations.

Learn More →

LLM-Generated Feedback

Analyze multi-agent interactions to improve coordination.

Learn More →