Hi all, first post here from the team at Hashlogics.
We’ve been using n8n as a key layer in an AI driven retention engine we’re building. The idea is to let LLM-based agents respond to user behavior events and autonomously trigger personalized actions across tools like Slack, Notion, and email.
Our typical flow:
- Webhook triggers from app events
- Python node calls an OpenAI agent for segmentation
- Based on output, we branch logic to follow up via Slack or queue an email
- Notion gets updated as a single source of truth
We chose n8n for its flexibility and transparency compared to no-code tools like Zapier, it gives us more control over debugging and scaling.
A couple of things we’re exploring:
- Managing fallback logic when LLM responses are ambiguous
- Structuring modular workflows that remain easy to test
- How others are layering AI safely into production workflows
If anyone’s working on similar agent-based automations or using AI for retention/lifecycle use cases, would love to learn from your approach.