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Hey guys, I've got a small workflow where I have a manage (tool agent) direct two other agents, one is a researcher and one is an editor. The idea is the user provides a topic and the researcher reseaches the topic and provide content and citation and the editor reviews that and provides feeback on what could be improved. This is working quite well except for the number of editoral reviews, I want the manager to allow up to two rounds of editoral review , but sometimes is allows 5 or 10 and I hit a limit I created in the model. I know I could do an external loop and add a counter but that somewhat defeats the purpose of a Manager agent, directing the "team" of agents. I have tried adjusting my prompt but its not consistent here is a shot of the workflow and the managers prompt, the initializeValues set node sets the property edit_round to 0 but I don't think the manager agent is really using that to count. My other idea is to create a "round counter" tool it can use maybe instead that will increment a counter using some simple persistence but I'm sure this has been solved before. I am using 4omini as the model for the manager agents so may try a more powerful mode and see if it can manage the counter. You are a highly organized Research Manager. Your objective is to guide a research process from initial topic to final output, ensuring quality through iterative editing, with a **strict maximum of 2 revision rounds**. todays date : {{ $now }} Available Tools: - researcher(query: string): Use this tool to perform initial research on a topic OR to revise previous research based on feedback. The query should be the topic or the revision instructions. **When calling the researcher, ensure the 'query' string includes instructions on desired source types (Industry Participants like executives, regulators, independent analysts such as Forrester, Gartner) and content types (statistics, quotes, key phrases).** Expected Output Structure: An array containing one object. That object has a property named 'output' which contains the structured research object (with research_text and references).

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I am finding significant latency on agents like 4o-mini when it has to answer a question and then generate structured JSON output. Its similar to nates examples of a project manager, it answer the users question like "whats the current budget" but then providing the project details for a UI like team members, stakeholders, recent updates, timeline. Using the tool agent to do both it hallucinated a lot, I create a structured output parser with another agent to create the JSON, that made it consistent but the latency went up from 4 seconds to 25. I did try some other smaller agents like grok and llama but those didn't even follow the prompt and were very inconsistent. I'm now doing two flows, one to get the quick answer the voice agent can read, and a 2nd call that the UI will use to get project data.. as I want a generative interface that display the "Details" of the user query on screen, and the voice agent summarizes.. If anyone has dealt with the latency of multiple LLM's and found ways to speed it up I'd be interested.