| Hi HN, I’ve been working on a system that runs multiple AI agents in parallel to perform structured research instead of generating a single summary response. One use case I tested recently was stock research. When you properly research a stock like NVIDIA, you usually open multiple tabs: - Financials
- Earnings reports
- Analyst sentiment
- Competitors
- Recent news
- Risks
- Market positioning Most AI tools generate one combined answer, which often becomes shallow or blended. So I built a workflow execution agents that: - Spawns multiple specialized agents at once
- Assigns each agent a focused responsibility (financials, competitors, risks, etc.)
- Runs them in parallel
- Normalizes structure
- Compiles everything into a single structured research report Instead of one AI response, you get multiple independent research threads that are merged into a coherent output. The goal isn’t “better summaries.”
It’s structured multi-angle research without manually orchestrating prompts. Here’s a short demo using NVIDIA stock: https://youtu.be/QBmFK843Kuo Would love feedback on: - Does parallel specialization meaningfully improve depth vs single-thread LLM prompts?
- Where else would this model be more useful (beyond stock research)?
- What would you want to see measured (quality benchmarks, latency, cost breakdown)? Happy to answer technical questions. |