Agent-managed personal CRM
Use Rowset when you want a trusted agent to maintain relationship context without turning every follow-up into a manual spreadsheet chore.
Keep recalled preferences in agent memory and current contact fields in the CRM dataset. The guide to AI agent memory vs structured state shows how to choose the authoritative home for each fact.
Starter shape
Create a people dataset. Use email as the index when contacts have reliable
email addresses, or person_id when one person can have several addresses.
People dataset indexed by email or person_id.
| name | company | relationship_stage | last_interaction | next_action | notes | |
|---|---|---|---|---|---|---|
| alex@example.com | Alex Morgan | Northstar Labs | follow up | 2026-07-01 | Send pricing notes | Asked for implementation examples |
| sam@studio.dev | Sam Lee | Studio Dev | warm | 2026-06-24 | Share demo recap | Intro from May conference |
| nora@acme.com | Nora Patel | Acme | waiting | 2026-06-28 | Check in after demo | Wants security details |
Agent jobs
- Add people and companies from meeting notes, emails, or chat summaries.
- Update relationship stage after each conversation.
- Find stale promises before they become dropped balls.
- Export a CSV or JSONL snapshot when you want a backup or handoff.
Dataset context and semantic schema
Add instructions that define stage meanings, follow-up rules, and what counts as
private notes. Mark email as an email column, last_interaction as a date,
and next_action as free text. Keep the agent honest: it should update rows
only from trusted notes or direct user instruction.
Connect it
Use MCP access first. If MCP is unavailable, use the Dataset API with a bearer API key. Public previews should stay off unless you deliberately want a read-only relationship board.