Parallel AI agents that research a stock simultaneously

Hacker News

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.

Visit product View on Hacker NewsFeb 25, 2026

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