Progressive Automation
A structured approach to moving from AI-assisted responses to fully automated customer support.
Progressive automation is Stylo's approach to building trust in AI-generated responses. Instead of choosing between "fully manual" and "fully automated," you start with human-reviewed suggestions and gradually increase automation as you build confidence in the AI's quality.
The automation ladder
| Stage | How it works | Who sends the response? |
|---|---|---|
| 1. Assist | Stylo generates a free-form suggestion based on the ticket and your knowledge base. The agent reviews, edits, and sends. | Agent |
| 2. Assist suggestions | Response workflows pre-generate structured responses based on your specific instructions. Agents review and send with one click. | Agent |
| 3. Conditional automation | Workflows that consistently produce high-quality responses are switched to auto-send for straightforward cases. Complex cases still go to agents. | AI (simple) / Agent (complex) |
| 4. Full automation | Proven workflows handle end-to-end resolution. Agents focus on escalations and edge cases. | AI |
Each stage builds on the last. The data from stage 2 (acceptance rates, edit distances) is what gives you confidence to move to stage 3.
How to progress
Stage 1 to 2: Add structure
When you notice agents repeatedly handling the same type of ticket:
- Create a response workflow with clear strategy instructions
- Set it to cache mode so it pre-generates suggestions
- Write a specific "when to use" description
The workflow now handles the research and drafting — agents just review and send.
Stage 2 to 3: Build trust with data
Monitor your workflow's performance:
- Acceptance rate — how often agents use the suggestion vs. dismiss it
- Edit distance — how much agents modify the suggestion before sending
- Confidence scores — how well the workflow matches the right tickets
When you see consistently high acceptance rates (>80%) with minimal edits over a sustained period, the workflow is a candidate for automatic sending.
Stage 3 to 4: Expand coverage
Once a workflow is reliably auto-sending:
- Review the escalation rules — make sure edge cases are properly routed to agents
- Monitor the quality check rejection rate
- Consider creating new workflows for related ticket types
What to measure
| Metric | What it tells you | Target for graduation |
|---|---|---|
| Acceptance rate | How often agents use the suggestion | Above 80% over 2+ weeks |
| Edit distance | How much agents change the text | Under 15% average modification |
| Confidence distribution | Whether the workflow matches the right tickets | Above 0.8 average confidence |
| Quality check pass rate | Whether generated responses meet quality standards | Above 90% |
| Escalation rate | Whether edge cases are properly caught | Stable, not increasing |
Internal notes as a stepping stone
The internal note automation mode is a useful intermediate step. Instead of sending directly to the customer, the AI posts the response as an internal note. This lets you:
- See exactly what the AI would have sent in a real ticket context
- Monitor quality at volume without customer risk
- Build confidence before switching to public replies
This is especially useful for workflows that handle sensitive topics (refunds, cancellations, complaints) where you want extra verification before enabling customer-facing automation.
Tips
- Don't rush to automate. The value of suggestions is that they make agents faster without any risk. There's no deadline to move to full automation.
- Automate the boring stuff first. Simple, high-volume, low-risk tickets (order status, shipping updates, thank-you responses) are the best candidates for early automation.
- Keep escalation rules tight. Every workflow that auto-sends should have escalation rules that catch edge cases. It's better to over-escalate than to send an inappropriate response.
- Review regularly. Even after automation, periodically review a sample of auto-sent responses to make sure quality hasn't drifted.