We rebuilt 47-node n8n flows until we admitted script-first AI workflows are just better

说明与用途

将 AI 逻辑从 n8n 可视化工作流迁移到专用服务，简化调试、提升可靠性并便于切换提供商。

问题

Complex n8n flows with many nodes and branches become fragile and hard to maintain when AI logic is embedded directly in the visual workflow, leading to debugging challenges, confidence loss, and difficulty in swapping providers or updating logic.

结果

Moving AI decision logic out of the visual workflow into a dedicated service reduced debugging time, improved confidence in changes, enabled easy provider swaps, and made the system more testable and maintainable.

工作流

• 1. n8n receives trigger (e.g., webhook or schedule).
• 2. n8n calls the internal AI service via HTTP POST with relevant data.
• 3. The service constructs prompts, validates responses with Zod, applies retry logic, and routes to the chosen LLM provider.
• 4. Service returns a stable JSON payload (category, urgency, summary, needs_human).
• 5. n8n receives payload and performs downstream actions (Slack, Zendesk, database writes).

证据

Use n8n, Make, and Zapier for orchestration. Put the AI decision layer in code.

来源

n8n just calls your service: curl -X POST http://internal-ai-service/triage ...

来源