From c4c31a6015cb292fbe1a1686254edf2f6ee3afc1 Mon Sep 17 00:00:00 2001 From: Ali Yaman Date: Tue, 11 Mar 2025 23:57:03 +0100 Subject: [PATCH] docs: clarify multi-agent orchestration description --- docs/multi_agent.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/multi_agent.md b/docs/multi_agent.md index cea3b901..aa1b6bc0 100644 --- a/docs/multi_agent.md +++ b/docs/multi_agent.md @@ -27,7 +27,7 @@ This pattern is great when the task is open-ended and you want to rely on the in ## Orchestrating via code -While orchestrating via LLM is powerful, orchestrating via LLM makes tasks more deterministic and predictable, in terms of speed, cost and performance. Common patterns here are: +While orchestrating via LLM is powerful, orchestrating via code makes tasks more deterministic and predictable, in terms of speed, cost and performance. Common patterns here are: - Using [structured outputs](https://platform.openai.com/docs/guides/structured-outputs) to generate well formed data that you can inspect with your code. For example, you might ask an agent to classify the task into a few categories, and then pick the next agent based on the category. - Chaining multiple agents by transforming the output of one into the input of the next. You can decompose a task like writing a blog post into a series of steps - do research, write an outline, write the blog post, critique it, and then improve it.