multi-agent-patterns

What this skill does

# Multi-Agent Architecture Patterns for Claude Code

Multi-agent architectures distribute work across multiple agent invocations, each with its own focused context. When designed well, this distribution enables capabilities beyond single-agent limits. When designed poorly, it introduces coordination overhead that negates benefits. The critical insight is that sub-agents exist primarily to isolate context, not to anthropomorphize role division.

## Core Concepts

Multi-agent systems address single-agent context limitations through distribution. Three dominant patterns exist: supervisor/orchestrator for centralized control, peer-to-peer/swarm for flexible handoffs, and hierarchical for layered abstraction. The critical design principle is context isolation—sub-agents exist primarily to partition context rather than to simulate organizational roles.

Effective multi-agent systems require explicit coordination protocols, consensus mechanisms that avoid sycophancy, and careful attention to failure modes including bottlenecks, divergence, and error propagation.

## Why Multi-Agent Architectures

### The Context Bottleneck

Single agents face inherent ceilings in reasoning capability, context management, and tool coordination. As tasks grow more complex, context windows fill with accumulated history, retrieved documents, and tool outputs. Performance degrades according to predictable patterns: the lost-in-middle effect, attention scarcity, and context poisoning.

Multi-agent architectures address these limitations by partitioning work across multiple context windows. Each agent operates in a clean context focused on its subtask. Results aggregate at a coordination layer without any single context bearing the full burden.

### The Parallelization Argument

Many tasks contain parallelizable subtasks that a single agent must execute sequentially. A research task might require searching multiple independent sources, analyzing different documents, or comparing competing approaches. A single agent processes these sequentially, accumulating context with each step.

Multi-agent architectures assign each subtask to a dedicated agent with a fresh context. All agents work simultaneously, then return results to a coordinator. The total real-world time approaches the duration of the longest subtask rather than the sum of all subtasks.

### The Specialization Argument

Different tasks benefit from different agent configurations: different system prompts, different tool sets, different context structures. A general-purpose agent must carry all possible configurations in context. Specialized agents carry only what they need.

Multi-agent architectures enable specialization without combinatorial explosion. The coordinator routes to specialized agents; each agent operates with lean context optimized for its domain.

## Architectural Patterns

### Pattern 1: Supervisor/Orchestrator

The supervisor pattern places a central agent in control, delegating to specialists and synthesizing results. The supervisor maintains global state and trajectory, decomposes user objectives into subtasks, and routes to appropriate workers.

```
User Request -> Supervisor -> [Specialist A, Specialist B, Specialist C] -> Aggregation -> Final Output
```

**When to use:** Complex tasks with clear decomposition, tasks requiring coordination across domains, tasks where human oversight is important.

**Advantages:** Strict control over workflow, easier to implement human-in-the-loop interventions, ensures adherence to predefined plans.

**Disadvantages:** Supervisor context becomes bottleneck, supervisor failures cascade to all workers, "telephone game" problem where supervisors paraphrase sub-agent responses incorrectly.

**Claude Code Implementation:** Create a main command that orchestrates by calling specialized subagents using the Task tool. The supervisor command contains the coordination logic and calls subagents for specialized work.

```markdown
<!-- Example supervisor command structure -->
1. Analyze the user request and decompose into subtasks
2. For each subtask, dispatch to appropriate specialist:
   - Use Task tool to spawn subagent with focused context
   - Pass only relevant context to each subagent
3. Collect and synthesize results from all subagents
4. Return unified response to user
```

**The Telephone Game Problem:** Supervisor architectures can perform worse when supervisors paraphrase sub-agent responses incorrectly, losing fidelity. The fix: allow sub-agents to pass responses directly when synthesis would lose important details. In Claude Code, this means letting subagents write directly to shared files or return their output verbatim rather than having the supervisor rewrite everything.

### Pattern 2: Peer-to-Peer/Swarm

The peer-to-peer pattern removes central control, allowing agents to communicate directly based on predefined protocols. Any agent can transfer control to any other through explicit handoff mechanisms.

**When to use:** Tasks requiring flexible exploration, tasks where rigid planning is counterproductive, tasks with emergent requirements that defy upfront decomposition.

**Advantages:** No single point of failure, scales effectively for breadth-first exploration, enables emergent problem-solving behaviors.

**Disadvantages:** Coordination complexity increases with agent count, risk of divergence without central state keeper, requires robust convergence constraints.

**Claude Code Implementation:** Create commands that can invoke other commands based on discovered needs. Use shared files (like task lists or state files) as the coordination mechanism.

```markdown
<!-- Example peer handoff structure -->
1. Analyze current state from shared context file
2. Determine if this agent can complete the task
3. If specialized help needed:
   - Write current findings to shared state
   - Invoke appropriate peer command/skill
4. Continue until task complete or hand off
```

### Pattern 3: Hierarchical

Hierarchical structures organize agents into layers of abstraction: strategic, planning, and execution layers. Strategy layer agents define goals and constraints; planning layer agents break goals into actionable plans; execution layer agents perform atomic tasks.

```
Strategy Layer (Goal Definition) -> Planning Layer (Task Decomposition) -> Execution Layer (Atomic Tasks)
```

**When to use:** Large-scale projects with clear hierarchical structure, enterprise workflows with management layers, tasks requiring both high-level planning and detailed execution.

**Advantages:** Mirrors organizational structures, clear separation of concerns, enables different context structures at different levels.

**Disadvantages:** Coordination overhead between layers, potential for misalignment between strategy and execution, complex error propagation.

**Claude Code Implementation:** Structure your plugin with commands at different abstraction levels. High-level commands focus on strategy and call mid-level planning commands, which in turn call atomic execution commands.

## Context Isolation as Design Principle

The primary purpose of multi-agent architectures is context isolation. Each sub-agent operates in a clean context window focused on its subtask without carrying accumulated context from other subtasks.

### Isolation Mechanisms

**Instruction passing:** For simple, well-defined subtasks, the coordinator creates focused instructions. The sub-agent receives only the instructions needed for its specific task. In Claude Code, this means passing minimal, targeted prompts to subagents via the Task tool.

**File system memory:** For complex tasks requiring shared state, agents read and write to persistent storage. The file system serves as the coordination mechanism, avoiding context bloat from shared state passing. This is the most natural pattern for Claude Code—agents communicate through markdown files, JSON state files, or structured documents.

**Full context delegation:** For com