agent-design

The best way to design multi-agent systems is to apply principles of simplicity, transparency, and modularity while selecting optimal workflow patterns like prompt chaining, routing, or parallelization for your specific task.

To choose the right AI agent workflow pattern, evaluate task complexity against patterns like orchestrator-workers for dynamic subtasks or evaluator-optimizers for iterative refinement, ensuring the architecture aligns with your specific data analysis requirements.

Use prompt chaining in AI agent architectures for sequential steps where each output feeds the next, and use parallelization when independent subtasks can run concurrently to reduce overall processing latency.

You need foundational prompt engineering and system design knowledge to implement an effective AI agent system, as this guidance focuses on architectural patterns and best practices rather than teaching basic LLM interaction concepts.

You can improve an existing AI agent system by applying modularity principles and optimizing workflow patterns, allowing you to refactor specific components like routing or evaluator-optimizers without a complete architecture rebuild.

A multi-agent system lacks transparency when workflow patterns are overly complex; fix it by applying modularity principles and utilizing clear patterns like prompt chaining to make data flow and decision steps explicit.