AI Agent Design Skill

ReAct interweaves reasoning and acting for single-agent tasks, while Plan-and-Execute separates planning from execution to improve multi-step task accuracy. This Skill provides patterns for both single-agent and multi-agent architectures.

Design a multi-agent system using a supervisor pattern by delegating tasks to specialized agents, or use hierarchical team patterns for complex coordination. This Skill covers architectures where a supervisor routes research, writing, and QA tasks.

Implement agent memory by separating short-term context for active tasks from long-term storage for persistent knowledge. This Skill provides best practices for integrating robust memory systems into your autonomous LLM architecture.

Use multi-agent architectures like Supervisor or Hierarchical Team patterns when tasks require specialized delegation, such as routing research, writing, and QA separately. Single-agent patterns like ReAct suit simpler, self-contained reasoning and tool use tasks.

Best practices for tool integration include defining precise tool schemas and implementing robust error handling for production deployment. This Skill outlines strategies for effective tool use and memory management within autonomous systems.

Yes, this Skill covers production deployment considerations for autonomous systems, including error handling, memory management, and tool definition. It provides comprehensive patterns for taking multi-agent systems from design to production.