Skip to main content
A central orchestrator coordinates multiple specialized agents.

​
Overview

The supervisor pattern uses a hierarchical structure where a central orchestrator acts as a supervisor, managing and coordinating multiple specialized AI agents. The supervisor:
  • Analyzes incoming requests.
  • Breaks down complex tasks.
  • Delegates to appropriate agents.
  • Aggregates and synthesizes results.
  • Delivers unified responses.
User Request → Supervisor → [Agent A, Agent B, Agent C] → Aggregated Response

​
When to Use

The supervisor pattern is ideal when:
  • Requests require dynamic agent selection at runtime.
  • Tasks can be decomposed into independent subtasks.
  • Multiple specialists should contribute to responses.
  • You want centralized control over coordination.
  • Conflict resolution between agents is needed.

​
Good Fit Examples

Use CaseWhy Supervisor Works
Customer serviceBilling, orders, and tech support agents work independently
Research assistantMultiple agents gather info from different sources
Travel bookingFlight, hotel, and car rental agents coordinate
Financial planningInvestment, tax, and insurance agents provide comprehensive advice

​
Architecture

​
Execution Flow

​
The Seven Stages

1. User Input
   └─ Request arrives at the application

2. Supervisor Analysis
   └─ Parse intent, identify required capabilities

3. Task Decomposition
   └─ Break complex request into subtasks

4. Agent Selection & Delegation
   └─ Route subtasks to specialized agents

5. Agent Execution
   └─ Agents process as per the assigned tasks

6. Result Aggregation
   └─ Collect outputs from all agents

7. Response Synthesis
   └─ Resolve conflicts, generate unified response

​
Example Conversation

User: "I need to return my order and get a refund. Also,
       can you explain your return policy?"

Supervisor Analysis:
├── Subtask 1: Process return request (Order Agent)
├── Subtask 2: Initiate refund (Billing Agent)
└── Subtask 3: Explain return policy (Support Agent)

Agent Execution:
├── Order Agent: Initiates return for order #12345
├── Billing Agent: Queues refund of $89.99
└── Support Agent: Retrieves return policy

Aggregated Response:
"I've initiated the return for your order #12345.
 A refund of $89.99 will be processed within 3-5
 business days once we receive the item.

 Our return policy: Items can be returned within
 30 days of purchase in original condition. Electronics
 have a 15-day window. Free return shipping is included."

​
Configuration Example

orchestration:
  pattern: supervisor
  model: gpt-4o  # Model for supervisor decisions

agents:
  billing_agent:
    name: Billing Agent
    description: Handles payments, refunds, and billing inquiries

  order_agent:
    name: Order Agent
    description: Manages orders, returns, and shipping

  support_agent:
    name: Support Agent
    description: Answers policy questions and general support

​
Key Capabilities

​
Modularity

Each agent focuses on a specific domain, making the system easier to build and maintain.

​
Scalability

Distribute complex tasks across multiple specialized agents. 
Single Request → Supervisor → Agent A → Agent B → Unified Response

​
Specialization

Each agent can use different models, tools, and knowledge optimized for their domain. 
billing_agent:
  model: gpt-4o  # Best for financial accuracy
  tools: [process_refund, check_payment_status]

support_agent:
  model: gpt-3.5-turbo  # Faster for simple lookups
  knowledge: [faq_database, policy_docs]

​
Flexibility

The supervisor dynamically selects agents based on request needs.

​
Fault Tolerance

If one agent fails, others can still complete their tasks.

​
Benefits

BenefitDescription
Centralized controlSingle point of coordination
Specialized agentsDomain experts handle domain tasks
Comprehensive responsesMultiple perspectives combined
Fault toleranceSystem continues if one agent fails

​
Limitations

LimitationMitigation
Supervisor overheadUse efficient models for coordination
Latency from aggregationSet agent timeouts
ComplexityStart simple, add agents as needed
Potential bottleneckOptimize supervisor logic

​
Example: Customer Service Assistant

name: Customer Service Hub
orchestration:
  pattern: supervisor

agents:
  - name: Billing Agent
    description: |
      Handles billing inquiries, payment processing, refunds,
      and invoice management.
    tools:
      - check_payment_status
      - process_refund
      - generate_invoice
    knowledge:
      - billing_faqs

  - name: Order Agent
    description: |
      Manages order status, shipping updates, modifications,
      and returns.
    tools:
      - get_order_status
      - update_shipping
      - initiate_return
    knowledge:
      - shipping_policies

  - name: Product Agent
    description: |
      Answers product questions, provides recommendations,
      and checks availability.
    tools:
      - search_products
      - check_inventory
      - get_product_details
    knowledge:
      - product_catalog

  - name: Support Agent
    description: |
      Handles general inquiries, policy questions, and
      escalations.
    tools:
      - create_ticket
      - escalate_to_human
    knowledge:
      - company_policies
      - support_faqs