orchestrationmediumcommon

Supervisor Pattern

Multi-agent workflows requiring clear coordination and audit trails

Overview

The Challenge

Complex workflows require coordination across multiple specialized agents, but without central oversight, agents may conflict, duplicate work, or miss critical handoffs.

The Solution

Employ a central supervisor agent that receives requests, decomposes them into subtasks, delegates to specialized agents, and synthesizes results into a coherent response.

When to Use
  • Workflows with clear task decomposition
  • Systems requiring audit trails and traceability
  • Environments where reasoning transparency matters
When NOT to Use
  • Simple, single-step tasks
  • Highly dynamic workflows where routing cannot be predetermined
  • Latency-critical applications (supervisor adds overhead)

Trade-offs

Advantages
  • +Clear coordination and visibility
  • +Easy to debug and trace decisions
  • +Centralized error handling
Considerations
  • −Can become a bottleneck at scale
  • −Single point of failure
  • −Added latency from coordination
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Deep Dive

Overview

The Supervisor Pattern is the foundational orchestration approach for multi-agent systems. A central supervisor agent acts as the coordinator, managing the flow of work between specialized worker agents.

Architecture

        ┌─────────────────┐
        │   Supervisor    │
        │     Agent       │
        └────────┬────────┘
                 │
    ┌────────────┼────────────┐
    ▼            ▼            ▼
┌────────┐  ┌────────┐  ┌────────┐
│Research│  │ Draft  │  │ Review │
│ Agent  │  │ Agent  │  │ Agent  │
└────────┘  └────────┘  └────────┘

Key Responsibilities

Task Decomposition

The supervisor analyzes incoming requests and breaks them into discrete subtasks appropriate for specialized agents.

Agent Selection

Based on subtask requirements, the supervisor routes work to the most capable agent. This may involve:

  • Static routing based on task type
  • Dynamic selection based on agent availability
  • Load balancing across equivalent agents

State Management

The supervisor maintains conversation context, intermediate results, and workflow state across the entire execution.

Result Synthesis

After worker agents complete their tasks, the supervisor aggregates outputs into a coherent final response.

Implementation Considerations

Supervisor Complexity

The supervisor must understand all agent capabilities without becoming a bottleneck. Keep supervisor logic focused on coordination, not domain expertise.

Error Handling

The supervisor should implement retry logic, fallback agents, and graceful degradation when worker agents fail.

Observability

Log all routing decisions and agent interactions for debugging and optimization.

When to Use

Good fit:

  • Workflows with clear task decomposition
  • Systems requiring audit trails and traceability
  • Environments where reasoning transparency matters

Poor fit:

  • Simple, single-step tasks
  • Highly dynamic workflows where routing can't be predetermined
  • Latency-critical applications (supervisor adds overhead)

Framework Support

Framework Implementation
LangGraph StateGraph with supervisor node
CrewAI Manager agent pattern
AutoGen GroupChat with admin
Semantic Kernel Planner pattern
  • Hierarchical Pattern: Multi-level supervision
  • Handoff Pattern: Explicit agent transitions
  • Blackboard Pattern: Shared state alternative
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Considerations

The supervisor can become a bottleneck in high-throughput systems. Consider hierarchical or distributed approaches for scale.

PreviousHierarchical Multi-Agent Pattern
Dimension Scores
Safety
4/5
Accuracy
4/5
Cost
3/5
Speed
2/5
Implementation
Complexitymedium
Implementation Checklist
Multi-agent framework
State management
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Tags
orchestrationcoordinationmulti-agentworkflowcentralized

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