Front office
Service & support agent
Triages, deflects, and routes inbound — with the plane decided per task.
The Service & support agent works the inbound queue across patient and prospect channels: it classifies and prioritizes tickets, drafts KB-grounded answers for the routine, and routes the rest to the right team with full context attached. It is safe because every reply is a draft a human approves before send, and the plane is decided per task — a prospect question stays Growth, a billing-or-chart question runs Operational with the PHI wall intact, enforced at the database grant.
Critical K+ 6.1 — Ward 4B charge nurse
escalates to physician in 04:12
AI proposal: repeat troponin — Dr. Chen
reminder RRULE: every 30m ×3
Transfusion start — second-nurse co-sign
acknowledged 14:02 · RN Aom
What it senses
The signals it watches for you
It reads across the systems you already run — on the medOS event substrate — and surfaces the issue while there is still time to act.
Inbound tickets
New and updated rows in support_tickets across web, app, email, SMS and contact-center channels — subject, body, sender, channel, current state.
Contact + interaction history
crm_contacts and crm_interactions for the sender — prior tickets, open opportunities, consent status, do-not-contact flags — to ground routing and tone.
Knowledge corpora (RAG)
The de-identified KB and policy corpora the LLM platform retrieves over, so routine answers are grounded in real documents, not invented.
PHI-touch signals
Detects when a ticket references a patient, an AN/HN or a charge — the trigger to escalate the task from Growth to the Operational plane, audited, never auto-answered with chart data.
SLA + ageing clock
Time-in-state and breach risk per ticket, so backlog at risk of breaching surfaces before a deadline is missed.
Sentiment + escalation cues
Complaint language, repeat contacts and churn-risk phrasing that flag an issue likely to escalate if it sits in the queue.
What it proposes
Drafted work, never an autonomous act
Each item lands in the Acknowledgement Inbox with its reasoning and a confidence score. Nothing is sent, charged or changed until a human accepts.
Deflect with KB answer
Draft reply to a prospect asking 'do you do knee replacements?' — cites the orthopedics service-line page, no patient record opened, Growth plane.
Route with context
Classify ticket #4821 as 'billing dispute', attach the contact's last 3 interactions, and route to the cashier queue with a one-line summary.
Escalate a flagged complaint
Flag a repeat-contact complaint (3rd touch in 5 days) as escalation-risk and draft a holding response for a supervisor to approve before send.
Promote to Operational plane
A patient asks 'why was I charged ฿4,200 on my AN?' — propose handing the task to billing on the Operational plane; the Growth agent cannot read the record itself.
The loop
Sense → propose → approve → execute
Sense
Reads new and updated support_tickets plus the sender's crm_contacts/crm_interactions, detects PHI-touch, and scores intent, urgency and SLA-breach risk.
Propose
Drafts a KB-grounded deflection reply, a routing decision with attached context, or an escalation flag — each with a reasoning trace and a confidence score, on the correct plane.
Approve
The proposal lands in the Acknowledgement Inbox as an agent_proposal; an agent accepts, edits or rejects, and that disposition is captured as the training label.
Execute
On accept, the same endpoints staff use fire — the messaging dispatcher sends the approved reply, auto-assign routes the ticket — with no shadow write path.
Capabilities
What it can do
Ticket triage & prioritization
Classifies every inbound ticket by intent and urgency and orders the queue, so backlog and breach risk surface before a human opens the inbox.
Routine deflection
Drafts KB-grounded answers to common, non-PHI questions over the RAG corpora — proposed for a human to send, never auto-sent.
Context-rich routing
Routes the non-routine to the right team via the delegation mesh and auto-assign, attaching contact history and a summary so the receiver starts with full context.
Proactive issue detection
Watches sentiment, repeat-contact patterns and SLA ageing to flag issues likely to escalate while there is still time to act.
Per-task plane decision
Keeps prospect questions on the Growth plane and promotes any record-touching task to Operational — the same agent, the plane chosen per task and enforced in the database.
After-call & after-chat work
Drafts call summaries and after-contact notes from the contact-center transcript into crm_interactions for an agent to confirm.
Consent-aware outbound drafting
Any outbound message it drafts honors channel consent and do-not-contact lists; a human approves before the messaging dispatcher sends.
Not a black box
Why it is safe to run
Autonomy without guardrails is a liability in a hospital. These are the constraints that make this agent safe to put to work.
Recommender-first on all outbound
Every reply, routing and escalation is a draft a human approves before send — a hard rule for anything patient-facing or regulated; complaint handling is never auto-sent.
Plane decided per task, enforced in the DB
A Growth-plane support task runs under a database role with zero grant on clinical tables; a record-touching question is promoted to the Operational plane, audited, with the PHI wall intact.
Consent + do-not-contact honored
Outbound rides marketing_consents and channel consent; nothing individually targeted without consent, and the messaging dispatcher is the only send path.
Audited per decision
Every proposal and disposition is logged in llm_audit_log under the named agent identity with its full RunnerStep reasoning trace; off by default with a per-tenant kill switch.
Data plane
It runs per-task across two planes — Growth for prospect/non-PHI tickets, Operational the moment a task touches a patient record — and the boundary is enforced at the database grant level, so the Growth-plane role literally cannot read a chart.
Operating characteristics
What changes when it runs
Always-on triage
Works the inbound backlog overnight; your team works the exceptions, not the queue.
Plane decided per ticket
Prospect questions stay Growth; record-touching tasks promote to Operational, audited.
Human-approved outbound
Every reply is a draft; nothing leaves the building without a sign-off.
Catches issues before they age
Flags repeat-contact and SLA-breach risk while there is still time to act.
Works on your stack
Reads and writes where you already work
On accept, it calls the very same endpoints your staff use — no shadow write path, no second source of truth.
Questions
Frequently asked
Can it answer a patient's question about their bill on its own?
No. The moment a ticket references a record, an AN/HN or a charge, the task is promoted to the Operational plane and handed to the right team — and any reply is still a draft a human approves. The Growth-plane support agent cannot read the chart at all; the wall is in the database grant.
Will it send replies without a human?
Never. Recommender-first is the rule on all outbound: the agent drafts, a human accepts, edits or rejects, and only then does the messaging dispatcher send. Complaint and clinical-adjacent replies are explicitly hold-for-approval.
How does it learn?
From your dispositions. Every accept, edit or reject is captured to the audit log as the training label — useful answers and routings are reinforced, rejected ones are not. It does not learn from anything written back to a record.
Does our data leave the building?
It does not have to. Inference can run on Ollama on your own hardware, so PHI stays in-house and air-gapped deployments are supported. Outbound only goes through your existing messaging dispatcher.
What does it actually write to?
On accept it uses the same endpoints staff use — it updates the ticket, routes via auto-assign, and sends the approved reply through the messaging dispatcher. There is no parallel system and no shadow write path.
Can it spam our prospect list?
No. All outbound honors marketing_consents and do-not-contact lists, nothing is individually targeted without consent, and a human approves every message before it sends.
Put Service & support agent on your floor
See it draft real work against your own workflows — every action under human sign-off.