Multi-Agent Patterns Skill

Overview

This skill addresses multi-agent system design, covering scenarios where supervisor patterns, swarm architectures, or agent coordination strategies are needed. Core insight: "Sub-agents exist primarily to isolate context, not to anthropomorphize role division."

Quick Start

1. •Identify need - Why multiple agents? (context limits, parallelism, specialization)
2. •Choose pattern - Supervisor, peer-to-peer, or hierarchical
3. •Design communication - Message passing, handoffs, state sharing
4. •Implement safeguards - Validation, timeouts, conflict resolution
5. •Monitor - Token usage, bottlenecks, failures

When to Use

• •Context window limits prevent single-agent solutions
• •Tasks benefit from parallel execution
• •Different domains require specialized knowledge
• •Complex workflows need coordination
• •Resilience through redundancy is required

Three Primary Patterns

1. Supervisor/Orchestrator

Structure: Central coordinator delegates to specialists and synthesizes results.

         [Supervisor]
        /     |      \
   [Agent A] [Agent B] [Agent C]
       ↑        ↑         ↑
       └────────┴─────────┘
           Results flow up

Best for:

• •Tasks with clear decomposition
• •Human oversight needs
• •Sequential dependencies
• •Quality control requirements

Key consideration: The "telephone game problem" emerges when supervisors paraphrase sub-agent responses incorrectly.

Solution: Implement forward_message tool enabling direct sub-agent-to-user communication:

def forward_message(agent_id: str, message: str, to: str = "user"):
    """Forward agent message directly without supervisor interpretation."""
    return {"from": agent_id, "message": message, "forwarded": True}

2. Peer-to-Peer/Swarm

Structure: No central control; agents communicate directly through protocols.

   [Agent A] ←→ [Agent B]
       ↑↓          ↑↓
   [Agent C] ←→ [Agent D]

Best for:

• •Flexible exploration
• •Emergent problem-solving
• •Parallel processing
• •Resilient architectures

Key requirements:

• •Predefined communication protocols
• •Explicit handoff mechanisms
• •Shared state management
• •Conflict resolution rules

3. Hierarchical

Structure: Layers of agents with strategy, planning, and execution tiers.

        [Strategy Layer]
              ↓
        [Planning Layer]
        /      |      \
   [Exec A] [Exec B] [Exec C]

Best for:

• •Complex organizational workflows
• •Multi-level abstraction
• •Clear separation of concerns
• •Enterprise-scale systems

Layer responsibilities:

• •Strategy: Goals, priorities, resource allocation
• •Planning: Task decomposition, scheduling, coordination
• •Execution: Actual work, reporting, feedback

Token Economics

Reality check: Multi-agent systems consume ~15x baseline tokens compared to single-agent approaches.

Optimization strategies:

• •Model selection often provides larger gains than more agents
• •Use smaller models for routine tasks
• •Reserve large models for synthesis and decisions
• •Implement aggressive context compression

Communication Patterns

Message Passing

class AgentMessage:
    sender: str
    recipient: str
    content: str
    message_type: Literal["request", "response", "broadcast"]
    requires_ack: bool = False

Handoff Protocol

class Handoff:
    from_agent: str
    to_agent: str
    context: dict  # Compressed relevant state
    task: str
    expected_output: str
    timeout_seconds: int = 300

State Sharing

class SharedState:
    version: int
    last_updated: datetime
    data: dict
    lock_holder: Optional[str] = None

    def acquire_lock(self, agent_id: str) -> bool: ...
    def release_lock(self, agent_id: str) -> bool: ...
    def update(self, agent_id: str, changes: dict) -> bool: ...

Implementation Guidance

Validation Requirements

• •Validate outputs before inter-agent transfer
• •Check message format and completeness
• •Verify agent capabilities before assignment
• •Validate state consistency after updates

Consensus Mechanisms

Recommendation: Implement weighted voting rather than simple majority:

def weighted_consensus(votes: List[Vote]) -> Decision:
    weighted_sum = sum(v.confidence * v.value for v in votes)
    total_weight = sum(v.confidence for v in votes)
    return Decision(value=weighted_sum / total_weight)

Safeguards

1. •

   Execution TTL - Prevent infinite loops:

   python

   max_execution_time = 300  # seconds
   max_iterations = 100
   

2. •

   Checkpoint Monitoring - Detect supervisor bottlenecks:

   python

   checkpoint_interval = 30  # seconds
   alert_threshold = 3  # missed checkpoints
   

3. •

   Circuit Breaker - Handle cascading failures:

   python

   failure_threshold = 3
   recovery_timeout = 60  # seconds
   

Best Practices

Do

1. •Start with simplest pattern that works
2. •Define explicit handoff protocols
3. •Include state management from the start
4. •Monitor token usage per agent
5. •Implement graceful degradation
6. •Log all inter-agent communication

Don't

1. •Use multi-agent for single-agent problems
2. •Assume agents will coordinate implicitly
3. •Ignore token costs during design
4. •Skip validation between agents
5. •Create deeply nested hierarchies
6. •Forget timeout handling

Error Handling

Metrics

Pattern Selection Guide

Is context window sufficient?
├── Yes → Single agent
└── No → Are tasks parallelizable?
    ├── Yes → Can agents work independently?
    │   ├── Yes → Peer-to-peer
    │   └── No → Supervisor with parallel workers
    └── No → Is there clear hierarchy?
        ├── Yes → Hierarchical
        └── No → Supervisor/Orchestrator

Related Skills

• •memory-systems - Cross-session persistence
• •parallel-dispatch - Concurrent agent execution
• •subagent-driven - Task execution pattern

Version History

• •1.0.0 (2026-01-19): Initial release adapted from Agent-Skills-for-Context-Engineering