Automating market intelligence with agentic workflows. 📊🛢️
I wanted a streamlined way to track how geopolitical events (specifically the Iran conflict) are impacting global markets and oil prices—without manually scanning news feeds every morning.
To solve this, I engineered a fully autonomous intelligence pipeline. The most interesting part of the build? I deployed the entire architecture directly from my IDE using Antigravity, connected to n8n via their native MCP (Model Context Protocol) integration.
Here is the technical breakdown of the workflow:
⚙️ Trigger: Runs automatically at 7:00 AM daily.
📡 Ingestion: Pulls real-time data from multiple RSS feeds tracking stock market indices and petrol price fluctuations.
🧠 Processing: Uses Llama-3.3-70b (via Groq) to synthesize the raw data, extract specific price movements, and format a highly structured executive summary.
📨 Delivery: Dispatches the final intelligence brief directly to my inbox.
It is fascinating to see how seamlessly AI agents can now interact with and build complex orchestration pipelines through MCP.
If any automation engineers or data analysts want to see the underlying n8n JSON architecture, let me know in the comments and I’ll share it.
