agent-patterns

Multi-agent system design patterns are architectural frameworks for coordinating specialized AI subagents. They include structures like fungible swarms, sequential pipelines, and hierarchical models to manage task delegation, orchestration, and context hygiene effectively.

To configure custom subagents for task delegation, you define their specific tools, models, and permissions. This ensures each agent is optimized for its designated role within the broader orchestration and batch processing workflow.

Use sequential pipelines for linear, step-by-step batch workflows, and hierarchical structures when you need a central orchestrator to delegate complex tasks to specialized subagents. Your choice depends on the required coordination and context management.

Failure recovery in multi-agent orchestration involves implementing strategies to manage agent coordination breakdowns. You need to establish context hygiene practices and configure fallback mechanisms to prevent cascading errors across your AI subagents.

Common mistakes in agent coordination include improper tool access configuration, failing to maintain context hygiene across subagents, and using mismatched delegation strategies for batch workflows. Proper configuration and pattern selection prevent these failures.