AI steps in automations
Categorize an intake note, pull fields from a document or draft a reply, as one step in any automation.
AI agents
Agents have no back door into the database. They call the same APIs as the web app, under the permissions of the person asking. They propose, a person decides, and every change is logged with a name on it.
Track patient callbacks and remind the on call nurse after 4 hours.
Builder agent
The builder agent turns a request into a proposal: the tables, fields, pages and automations it would add. You see a preview before anything changes. It starts from vetted pack shapes, not guesses.
Track patient callbacks and remind the on call nurse after 4 hours.
How many high acuity callbacks were late this week, by day?
7 late callbacks this week. Tuesday had the most, during the evening shift change.
{
"table": "Tasks",
"filter": {
"type": "Callback",
"acuity": "High",
"late": true
},
"groupBy": "day",
"aggregate": "count"
}Read only · runs as youAnalyst agent
Questions become structured queries, never raw SQL. The agent reads only what you can read, shows the query it ran and lets you pin the chart to a page.
Everywhere else
Categorize an intake note, pull fields from a document or draft a reply, as one step in any automation.
Point your own assistants at an app through the Model Context Protocol. Read only, scoped by API key.
Agent changes land in the event log with the agent and the approving person both recorded.
Guardrails
All AI traffic goes through the HealthTable model gateway. The rules live there, so no feature can skip them.
Apps that hold patient data send prompts only to model providers under a BAA. The gateway enforces it.
Spend budgets and rate limits per app, with redacted logging of every call.
Agents draft and propose. In clinical apps a person confirms before anything changes.
Bring a process your team runs today. We will walk through how it looks on HealthTable, what is ready now and what is still on the roadmap.