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multi-agent-patterns

Coordinates multiple AI language-model agents via supervisor-orchestrator and handoff protocols for task decomposition and aggregation.

3|Updated Mar 22, 2026
Search Tags:#patterns#multi-agent#orchestration#swarm#coordination#handoffs#context-isolation
One-click install
npx skills add https://github.com/0xharryriddle/codex-field-kit --skill multi-agent-patterns-0xharryriddle
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: multi-agent-patterns
Source: https://github.com/0xharryriddle/codex-field-kit/tree/main/archive/upstream/chasebuild-agent-skills/context-engineering/skills/multi-agent-patterns
Command: npx skills add https://github.com/0xharryriddle/codex-field-kit --skill multi-agent-patterns-0xharryriddle

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Multi-agent architectures address context limitations by fragmenting tasks across specialized agents and a coordinating supervisor, enabling scalable reasoning and parallel execution.

Core Features & Use Cases

  • Provides supervisor/orchestrator, peer-to-peer swarm, and hierarchical delegation patterns to divide work and improve throughput.
  • Emphasizes explicit context isolation and robust handoff protocols to prevent context bleed and preserve decision fidelity.
  • Includes practical examples and guidance for research, development, and production workflows requiring multi-agent coordination.

Quick Start

Demonstrate a simple task where a supervisor delegates subtasks to two workers and aggregates their results.

Frequently Asked Questions about multi-agent-patterns

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I coordinate multiple language-model agents to improve task throughput?▼

Multi-agent coordination organizes specialized agents into distinct roles with a supervisor orchestrating task decomposition, assignment, and result aggregation to enable scalable parallel execution.

What are the best patterns for preventing context bleed in parallel agent reasoning?▼

Context isolation patterns prevent context bleed by enforcing explicit boundaries and robust handoff protocols between agents, preserving decision fidelity when fragmenting complex tasks across specialized roles.

When do I need a supervisor-orchestrator architecture for multi-agent systems?▼

A supervisor-orchestrator architecture is needed for complex research, planning, and execution tasks requiring hierarchical delegation, peer-to-peer swarm coordination, and structured task decomposition.

How do I set up agent handoffs for hierarchical delegation workflows?▼

Hierarchical delegation workflows require a lightweight communication layer managing agent handoffs, where a supervisor delegates subtasks to specialized workers and aggregates their results.

What are the limitations of using peer-to-peer swarm patterns for complex execution tasks?▼

Peer-to-peer swarm patterns require robust handoff protocols and context isolation to maintain decision fidelity; without them, coordination overhead can negate throughput gains in complex execution tasks.

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