Some tasks require iteration - generating, evaluating, and refining until quality standards are met. Others need dynamic orchestration - a central coordinator breaking down novel problems and delegating to specialists. In this post, I’ll cover two sophisticated patterns that enable these capabilities: the evaluator-optimizer loop and the orchestrator-worker architecture.
Sequential chains work well when tasks have a clear order, but real-world problems often require more flexibility. Sometimes you need to route tasks to different specialists based on their content. Other times, multiple agents should work simultaneously on different aspects of a problem. In this post, I’ll cover two powerful patterns: routing for intelligent task dispatch and parallelization for concurrent processing.
When a single prompt isn’t enough, we chain them together. Prompt chaining is one of the most practical patterns for building AI workflows - breaking complex tasks into focused steps where each agent’s output feeds into the next. In this post, I’ll explore how to design, validate, and implement effective prompt chains.
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