building-multiagent-systems

What problem does it solve?

This skill provides a structured approach to architecting and coordinating multiple AI agents that collaborate to complete complex tasks, ensuring predictable behavior through a four-layer architecture and reusable coordination patterns.

Core Features & Use Cases

• Four-Layer Architecture: Enforces deterministic layers (Reasoning, Orchestration, Tool Bus, Deterministic Adapters) for modular, testable agent systems.
• Seven Coordination Patterns: Offers proven patterns (Fan-Out/Fan-In, Sequential Pipeline, Recursive Delegation, Work-Stealing Queue, Map-Reduce, Peer Collaboration, MAKER) to match task needs and scale.
• Tool Coordination & Schema-First Tools: Promotes safe tool usage, explicit schemas, and controlled sub-agent tool access with permission inheritance.
• Production Hardening: Includes cascading stop, orphan detection, cost tracking, session persistence, and checkpointing to improve reliability in real-world deployments.
• Real-World Applicability: Suitable for complex automation, code review workflows, data processing pipelines, and scalable orchestration across heterogeneous environments.

Quick Start

Launch a small multi-agent task by spawning a few specialists, assign deterministic tools, and aggregate results with cascading stop and cost tracking for safe execution.