When the Global AI Hackathon Series with Qwen Cloud was announced, I set out to tackle a pervasive B2B sales problem: account executives and sales development reps (SDRs) spend over 70% of their working hours on manual research, contact hunting, and email drafting.
My goal was to build Ideal Customer Finder (Track 4: Autopilot Agent)—a 9-node autonomous state machine that takes a natural language Ideal Customer Profile (ICP), searches the live web, enriches contacts, detects buying signals, scores accounts using dual AI/vector reasoning, and drafts personalized outreach.
Moving from architectural theory to a live, production-ready system on Qwen Cloud and Alibaba Cloud involved real engineering friction, deep debugging, and critical architectural pivots. Here is the unvarnished build story.
1. Taming the Token Explosion: Optimizing MCP Tools
My pipeline relies on live web search to discover matching companies and executive contacts. To integrate web search, I used the Nimble MCP server exposed through Qwen's Responses API (type: "mcp" tool calling nimble_search).
