FAQ — What data does Self-Learning use?
A short answer to the most common question.
What gets used
In the regular Self-Learning feedback workflow, Self-Learning uses:
- Flagged AI responses from your conversations — flags submitted from the Conversations inbox, the Agent Stack test sandbox, and the Monitor.
- The annotations your team writes on those flags — the comment text and the intent category (missing info, too verbose, incorrect, tone, routing error, knowledge gap, other).
- The conversation context around the flagged message — the messages immediately before and after, and the specialist that produced the flagged reply.
That’s it for the regular feedback workflow. Nothing else feeds that proposal generator.
Owners and superadmins may also have access to Atender Supervisor, a separate gated workflow. Supervisor can analyze recent live AI conversations, create supervisor-sourced findings and proposals, and backtest those proposals before approval. Supervisor-sourced rows are kept separate from normal feedback groups and lists. Supervisor-generated results can also be read through tenant API keys or MCP connections that include the supervisor:read scope. Those reads stay tenant-scoped: the tenant is derived from the key, full Self-Learning opt-in is checked on each request, and the API can only read existing results — it cannot start or rerun analysis.
What does NOT get used
In the regular Self-Learning feedback workflow:
- Conversations from other tenants. Self-Learning is scoped to your tenant. Your data never shapes another tenant’s AI, and theirs never shapes yours.
- Unflagged conversations. Conversations that nobody marks as needing improvement are never fed into the regular proposal pipeline. The AI doesn’t second-guess responses on its own in this workflow.
- Customer PII for training. Proposals are generated from the substance of the issue, not raw customer identifiers. Anything matching your redaction rules on capabilities is masked before it reaches Self-Learning.
Where do approved changes apply?
Only to your tenant’s stack. An approved prompt edit modifies your specialist agent. An approved KB-article proposal creates a draft in your Knowledge Base. Nothing leaks across tenants.
Can I disable it?
Yes. Self-Learning is configurable per tenant. If you turn it off, no new feedback reports get processed and no new proposals are generated. Existing groups, proposals, and audit-log entries are preserved — you can re-enable later without losing history.
Who can see flagged content?
Anyone on your tenant with access to Self-Learning. By default, that’s admins and supervisors. Review-only users can see Feedback Groups and the Audit Log; full-access users can also see and act on Staged Changes. Ask your admin to adjust roles if you need to widen or narrow that.