11d • General discussion
Refactoring from Parallel to Sequential Agents for better context
I recently posted V1 of my Agency Operations Engine, which used a parallel architecture where the Operations, Financial, and Culture agents ran simultaneously.
After testing, I realized the final synthesis was weak because the agents weren't learning from each other. I completely rebuilt the flow to be sequential.
The Logic Change:
- Ingestion: Added a "Wait" node to ensure all files are fully embedded in Pinecone before analysis starts.
- Sequential Chaining: Instead of running in parallel, the data now flows linearly: Operations Analyst -> Financial Risk Analyst -> Culture Analyst.
- Partner Synthesis: In V1, I just used a Set node to combine the text. In V2, I added a specialized "Partner Synthesis Agent" that acts as a Senior Partner (McKinsey style), taking the previous outputs to generate a "Strategic Business Health Audit" before writing to Google Docs.
The output quality is significantly higher when you force the LLM to build context layer by layer rather than trying to stitch three simultaneous outputs together.
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Refactoring from Parallel to Sequential Agents for better context
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