Your meeting summarizer is quietly doing three jobs in one prompt
The usual way to summarize a meeting with an LLM is one prompt: "Here's the transcript — give me a summary, pull out the action items, and tell me how everyone felt." One call, one model, one blob of text back.
It works on a demo and frays on a real transcript. Those are three different jobs with three different shapes. A summary wants to flow as prose. Action items want to be a strict list with an owner on every row. Sentiment wants one verdict per speaker. Cram them into a single prompt and they fight: the model pads the summary into the action items, or it forgets to tag a speaker, or the "action items" come back as a paragraph you now have to parse by hand. You also pay for all of it serially, and you get back unstructured text when half of what you wanted was structured data.
There's a cleaner shape. Run three specialists, each doing exactly one job, each at its own temperature, two of them returning typed objects instead of prose — and run them at the same time, because none of them needs another's output. Then a fourth agent merges the three results into one report.
This post builds exactly that, from the meeting-summarizer cookbook example in open-multi-agent. The whole thing is ~280 lines of TypeScript and the parallelism is the point.
