Most contact centers find every training need through QA on live calls — a scored conversation that tells a supervisor something went badly on a real customer. That says who needs work but not whether they have gotten better. The missing step in between is practice: a place to make mistakes without a customer on the other end.
Devotel Orbit's Practice Studio is that place. It lives under Quality → Practice in the dashboard, and it trains the human agent — not the AI — by having an AI play the customer.
What a Practice Studio is
The name is literal. A scenario is a rehearsed conversation: a persona for the AI customer ("angry about a duplicate charge, impatient but reasonable once heard"), an objective ("de-escalate, confirm the duplicate, offer a refund"), and a rubric — one criterion per line — that grades the run. The agent talks back and forth with the simulated customer; the scorer grades the conversation against each rubric item and returns a 0–100 score plus short written coaching feedback.
That inversion — a person being assessed while a model acts as the customer — is what distinguishes it from the AI-agent QA content elsewhere on this blog. Pipelines for evaluating the AI voice agent itself are a different topic; this page is about training people.
The surface is role-aware. Owners, admins, and supervisors get a "New scenario" button and see the whole team's session history. Other roles browse the active scenarios, start runs, and see their own history — while the API auto-scopes the session list, so an agent never sees anyone's runs but their own.
How supervisor authoring works
Scenario creation happens through POST /api/v1/quality/practice/scenarios, available to owner, admin, and supervisor roles. The dashboard form asks for:
- Title — for example, "Angry customer disputing a charge."
- Channel — chat, voice, or email (pick the medium the trainee works in).
- Difficulty — intro, intermediate, or advanced, so the library can form a progression.
- Customer persona — the behavioral spec the AI follows: mood, situation, what the simulated customer wants.
- Objective — what a good agent outcome looks like, optional but useful.
- Scoring criteria — the rubric, one line per criterion. The roleplay is graded against every line.
Once a scenario is marked active it shows up in every teammate's Scenarios tab. Reviewers can also filter to active-only; agents see GET /api/v1/quality/practice/scenarios?active=true filtered automatically.
Running a session and getting scored
An agent starts a run with POST /api/v1/quality/practice/sessions carrying a scenario_id. The conversation happens in-app; each turn is exchanged with the AI persona, and the run completes when the trainee ends it. On completion the scorer produces:
- an overall score from 0 to 100,
- short feedback naming which rubric criteria passed and where the conversation went off-script, and
- the turn count, status (
in_progress,completed, orabandoned), and timestamps.
All of that lands in the My sessions tab, and a KPI strip at the top of the page summarizes the whole history: runnable scenarios, total runs, average score across completed runs, and best score. Reviewers see the same average and best across the team's runs, so a supervisor can spot the agent whose average lags the scenario's difficulty and coach that specific rubric item.
What it does not do
The roleplay is an in-app simulation, end to end. No PSTN or SMS traffic is generated — the "voice" and "email" channel labels pick the medium the trainee rehearses in, not a real dial or send. Practice runs never touch a real customer, and the session list stays inside the tenant. This matters for compliance: a scenario that rehearses a regulated disclosure (identity verification, recording notice, refund terms) still exercises the agent's knowledge of the disclosure without contacting anyone.
It also does not replace live-call QA. Evaluations still sample real conversations; the leaderboard still gamifies the team's live metrics. Practice Studio sits between the two: evaluations surface a weakness, and practice closes it before the leaderboard reflects it.
Where it sits against the other quality surfaces
Three sibling surfaces live under /quality, and they deliberately split the loop:
- [Evaluations](/quality/evaluations) — the record of LLM-judged scores on real conversations. Tells a supervisor which conversations failed which rubric.
- Practice — the remediation loop for human agents. Rehearsal happens here, off live traffic.
- [Leaderboard](/quality/leaderboard) — points and rankings fed by QA scorecards, handled volume, and CSAT across human and AI agents alike.
A working coaching routine runs through all three: the evaluation flags the gap, the supervisor authors (or assigns) a practice scenario matched to that gap, the agent runs it until the rubric passes, and the leaderboard reflects the recovered live-call quality. Practice Studio is the practice leg of that loop; it exists because remediation-by-live-call is an expensive way to train.
Example: the insurance claim intake scenario
A home-insurance team found their intake adjusters skipping the incident-date question on calls that later escalated. The supervisor authors:
- Title: "Claim intake — storm damage, upset policyholder"
- Channel: voice · Difficulty: intermediate
- Persona: "Homeowner after storm damage. Upset, talks fast, gets vague on dates unless prompted. Calms down once the agent shows they have a full record."
- Objective: "Capture a complete claim record — incident date, damage description, policy number — before routing to an adjuster."
- Rubric:
- "Captured the incident date before proceeding" - "Verified the policy number" - "Got a full damage description" - "Closed with next steps and a reference"
Agents run the scenario; the scorer checks each line. A run that goes "I have the storm damage noted" without an incident date fails the first criterion explicitly, and the feedback says so. The agent re-runs until intake is complete on the first shot; the supervisor watches the team-average on that rubric item climb in the sessions list.
Frequently asked questions
Who can create practice scenarios?
Owners, admins, and supervisors author and manage scenarios; the API returns 403 to other roles. Agents and other roles can browse the active library and run sessions against it.
Does practice traffic ever reach a real customer?
No. The roleplay is an in-app simulation only — no PSTN, no SMS, no outbound traffic of any kind. The channel label on a scenario (chat, voice, email) describes the medium the trainee rehearses, not a live send.
How is a scored session graded?
The auto-scorer grades the full conversation against each criterion in the scenario's rubric, returns a 0–100 overall score, and writes short feedback naming which criteria passed. In-progress and abandoned runs show no score; the average-score card only counts completed, graded runs.
Can an agent see other teammates' sessions?
No for non-reviewer roles — the session list auto-scopes to the caller. Reviewers (owner, admin, supervisor) see the team's runs, including each agent's name on each row.
What happens when a scenario is retired?
Setting is_active false removes it from agents' runnable picker. Reviewers still see it in the management list (filtering to active-only shows what trainees see), so a scenario can be restored without rewriting it.
Getting started
Open Quality → Practice in the dashboard. A supervisor authors the first scenario; the KPI strip fills in as the team runs. Pair it with a weekly evaluations review, and the loop — flag weakness, author a scenario, rehearse, watch the score climb — runs entirely in one Quality area.