Aller au contenu principal

Operational use cases

Eight concrete AI-agent workflows.

These examples explain the possible scope, human control and measurements to define. They are not presented as published customer results.

Professionals map a business request before routing and human validation.
Illustrative work setting — the people shown are not presented as Optimal Prompt AI staff or clients.

Inbound enquiry agent

Prepared framework — evidence pending

Starting context

Requests arrive through several channels and require the same initial checks.

System scope

Classify, request missing details and prepare routing under defined rules.

Human control

A person reviews uncertain or high-value requests.

Evidence required

Baseline volume, processing time, error rate and an authorised before/after period.

Proposal preparation agent

Prepared framework — evidence pending

Starting context

A team repeatedly assembles proposals from approved information and templates.

System scope

Prepare a first draft while preserving controlled prices, terms and source data.

Human control

A person approves every proposal before it is sent.

Evidence required

Version history, preparation time, correction categories and authorised examples.

Monitoring and synthesis agent

Prepared framework — evidence pending

Starting context

Recurring research is performed across an agreed list of authorised sources.

System scope

Collect, deduplicate and summarise findings while retaining source links.

Human control

A person checks material facts and decides what is shared.

Evidence required

Source list, completeness criteria, review log and measured comparison period.

Three-agent follow-up workflow

Prepared framework — evidence pending

Starting context

Several repetitive handoffs occur between qualification, record creation and follow-up.

System scope

Three agents handle three defined tasks with explicit handoff rules.

Human control

Exceptions and final external messages remain under human control.

Evidence required

Workflow map, handoff failures, completion rate and authorised operational records.

Voice agent for inbound calls

Prepared framework — evidence pending

Starting context

Recurring calls follow a known initial qualification script and often require the same information.

System scope

Answer within an approved script, collect required details and prepare routing or booking.

Human control

Sensitive, uncertain or high-value calls are transferred to a person.

Evidence required

Call categories, transfer rate, booking completion and authorised quality reviews.

Appointment and reminder agent

Prepared framework — evidence pending

Starting context

Appointment requests and follow-ups are handled manually across email, calendar and CRM.

System scope

Check the agreed availability rules, prepare a booking and update compatible tools.

Human control

A person handles exceptions, conflicts and any commitment outside the approved rules.

Evidence required

Request volume, incomplete bookings, rescheduling rate and documented follow-up time.

Customer-service triage agent

Prepared framework — evidence pending

Starting context

Support requests arrive with different urgency, completeness and operational impact.

System scope

Classify the request, retrieve approved information and prepare the next response or assignment.

Human control

Complaints, sensitive data and uncertain answers are escalated to the responsible team.

Evidence required

First-triage time, escalation categories, correction rate and authorised ticket samples.

Operational reporting agent

Prepared framework — evidence pending

Starting context

Teams repeatedly collect the same authorised indicators from several compatible sources.

System scope

Consolidate the selected data, flag missing values and prepare a structured report.

Human control

A person verifies figures, interpretations and any decision based on the report.

Evidence required

Source coverage, preparation time, correction log and an authorised comparison period.

A real result starts with measurable facts.

The assessment records the current task and environment so the future project can define its own baseline and evidence.

Start my AI assessment