governed ai operators · built for real front desks

AI agents that answer, triage, book, and escalate.

OperatorFlow installs an AI operations layer for small teams: agents that handle calls, messages, intake, bookings, follow-ups, and daily summaries inside your existing tools. Humans approve the important parts.

Get a workflow teardownsend one messy workflowView teardown examplessee three market probes
1 agentlive in 10 business days
approval-firstno risky sends by default
audit trailevery action reviewable
your toolscalendar, inbox, CRM, docs
front-desk-agent · intake-to-escalation● running
inboundclassifyai draftcrm synchuman oksent
● signal = active· dashed = pending5 nodes · 5 edges · 1 branch
/ why normal automation is not enough

The work is not just lead capture. It is operational judgment.

A contact form can create a lead. A CRM can store it. The hard part is what a human front desk does next: ask the right questions, know what matters, route exceptions, update systems, and keep the business moving.

01 ·

Calls and messages create work, not just records.

A parent asks about toddler availability. A pet owner describes symptoms. A new client sends half the documents. Each case needs context, not a tag.

02 ·

Most tools stop before the handoff.

Voice bots answer. CRMs store. Schedulers book. Small teams still need someone to stitch the outcome together and decide what deserves attention.

03 ·

Agents need a harness, not a hype demo.

Useful agents need approved knowledge, tool access, escalation rules, human approval, logs, monitoring, and a clean way to hand work back.

/ what we build

A governed AI operator inside your existing workflow.

The product is not a chatbot. It is the operating harness around the agent: tools, memory, approval gates, escalation paths, daily summaries, and audit logs your team can actually trust.

front-desk-agent · call + message intakeready
00:00
Answer or capture
phone / form / sms / email
00:20
Classify intent
booking · urgent · policy · docs
01:10
Collect context
structured questions · source-backed
02:00
Route next action
book, draft, escalate, or queue review
example pattern · appointment-heavy teams
approval-queue · human in controlready
review
Show the decision
intent · confidence · sources
approve
One-click send
staff edits or approves before external action
escalate
Wake a human
urgent, sensitive, low-confidence, policy gap
learn
Tune the rule
approved corrections become operating context
guardrail pattern · no blind automation
ops-digest · daily operator summaryready
am
Open items
bookings · callbacks · missing docs
risk
Exceptions
urgent calls, unhappy customers, policy gaps
metrics
What changed
response time, volume, approvals, escalations
next
Recommended actions
the queue your team should handle first
operating pattern · keeps the system alive
/ try it · simulated

One inbound message. One structured next action.

The demo is intentionally local and canned. The point is the shape of the harness: classify, collect context, draft, route, and keep a human in the loop.

front-desk-agent · interactive sketchidle · waiting for input
inbound · raw0 chars
agent output · human review
↳ classification, routing, draft response, and escalation notes appear here.
try:
/ first offer

Front Desk Agent Sprint.

A fixed-scope beta build for one operational agent. We map the work, connect the tools, install the guardrails, and monitor the first month.

sprint.yml · front-desk-agentv0.4 · founding beta
duration10 business days
scope1 operational agent + intake map + approval harness
channelsphone/message/form/email depending on fit
reviewAI drafts and routes; humans approve risky external actions
deliverablesrunning agent · source-backed knowledge · audit log · runbook
pricefounding beta CA$2.5k-5k fixed after teardown
retaineroptional CA$750-2k/mo monitoring + tuning + new flows
availabilityavailable · 1 sprint slot open
01 ·

Map the real front desk.

Inputs, tools, scripts, policies, calendars, exception paths, failure modes, and what staff should never delegate to AI.

02 ·

Build the harness.

Agent flow, approved knowledge, tool actions, approval queue, escalation rules, summaries, logging, and a practical operator view.

03 ·

Launch with monitoring.

Start narrow, review every external action, tune against real traffic, then decide whether to hand off, retain, or productize the workflow.

/ why us

OperatorFlow is for small teams that want agentic operations without handing their business to a black box.

arnab saha · founder · operatorflow
// building agent runtime observability at agentweave.dev

credibility.yml
systemsplatform, data, cloud, and AI operations leadership
agentweaveopen-source observability for AI agent systems · agentweave.dev
modelgoverned agents · tool access · approval gates · audit logs
out-of-scopeblind auto-send · legal/medical advice · strategy decks · prompt packs
ownershipyou keep the workflow map, runbook, and system context
locationCanada-based · remote across NA/EU
/ objections

The boring questions matter.

Do we need to replace our tools?

No. The first pass works around the tools you already use: inbox, phone, calendar, CRM, spreadsheet, documents, and task systems.

Is anything sent automatically?

Not by default. V2 is approval-first. The agent can draft, classify, book tentatively, or escalate; external actions start with human review.

What happens after 30 days?

Either you operate the runbook yourself, keep OperatorFlow on a light retainer, or expand the agent into a second workflow.

Why not buy an AI receptionist?

You should, if a generic receptionist solves the whole problem. OperatorFlow is for cases where the call creates downstream work across docs, calendars, cases, staff, and escalation rules.

/ start with one workflow

Send the front-desk workflow your team keeps patching by hand.

We will send back a practical teardown: what to automate, what to keep human, what tools to connect, and whether a sprint is worth doing.

Get a workflow teardown[email protected] · reply within 1 business dayavailable · 1 sprint slot open