AI Workforce · Module
Training Gym & Simulation
Practise with synthetic customers, improve through versioned releases only
Training Gym & Simulation lets agents practise against synthetic customers before they meet real ones, and turns consented production traces into new scenarios and eval cases. Agents improve only through tested, versioned releases.
Practice before production
A Digital Employee should meet difficult customers in a simulator first. The Synthetic Customer Simulator v0 is planned for W1 and simulator lite is inside the Year-1 boundary. The Training Gym pipeline is planned for W3 and the full Agent Simulator & Sandbox for W4.
The simulator
Synthetic personas speak English, Hindi, Tamil and code-mixed text, starting with SDR and Support roles and extending to collections, content review and appointment roles. Scenario scripts, objection libraries and score rubrics produce reproducible scores with fixed seeds, with at least 100 scenarios per launch role. Simulation runs are tagged as simulation, Actions are simulated, and there is no real contact or side effect. Results feed the "Simulation" dimension of the Eval Coverage Score. Voice simulation with noisy transcripts and Indic accents is planned.
Tenant practice
Tenants can run a rented or built agent against simulator scenarios with their own playbook and knowledge scopes before going live, to see how it handles their objections and policies.
The Training Gym pipeline
Agents do not "learn on their own" in production. The Gym takes tenant-consented traces, runs redaction and consent checks, labels failures and successes, and turns them into scenarios and eval cases. Failure clustering from production traces proposes new scenarios. Promotion gates apply per agent version with rollback. Version experiments on live traffic are planned with controls.
Isolation and privacy
Per-tenant adapters are trained only on that tenant's opted-in data, served with tenant-bound ids and tenant-key encryption. Aggregate signals across tenants get a privacy review: no raw content, minimum aggregation thresholds and monthly sample review. Any tenant can set "exclude from all aggregate signals" regardless of plan. Defences against reward hacking and contamination protect both training and evals. The full sandbox will run agents against complete synthetic tenants generated with Synthetic Data Studio.
Features
What Training Gym & Simulation includes
Synthetic Customer Simulator
Personas in English, Hindi, Tamil and code-mixed text.
Scenarios, objections and rubrics
Reproducible scores with fixed seeds.
Scenario coverage
At least 100 scenarios per launch role.
Side-effect-free runs
Simulation mode with simulated Actions and no real contact.
Eval Coverage feed
Results feed the Simulation dimension.
Tenant role practice
Test with your own playbook and knowledge before going live.
More simulator personas
Collections, content review and appointment roles.
Training Gym pipeline
Consented traces to labelled scenarios and eval cases.
Failure clustering
Production failures propose new scenarios and evals.
Promotion gates with rollback
Per agent version.
Per-tenant adapters
Trained only on that tenant's opted-in data.
Agent Simulator & Sandbox
Full synthetic tenants before promotion (W4).
Contamination defences
Against reward hacking and leakage.
Aggregate-signal privacy
Thresholds, no raw content and a tenant opt-out.
Workflow
Trace to release
- 1
Collect tenant-consented traces
- 2
Redact and check consent
- 3
Label failures and successes
- 4
Create scenarios and eval cases
- 5
Train or adjust the candidate version
- 6
Pass promotion gates
- 7
Release as a new version with rollback
AI
AI skills and Digital Employees
Synthetic customer personas
Simulated customers with objections across languages.
Failure clustering
Groups production failures into new scenarios.
Rubric scoring
Reproducible scoring of simulated conversations.
Digital Employees that use this module
- AI SDR — Inbound Lead Responder
- Support Triage & Reply Assistant
- Collections Reminder Assistant
They act through the same governed Actions as people; risky steps wait for approval.
Frequently asked questions
Does the agent learn from my customers automatically?
No. Improvements come only through tested, versioned releases built from consented traces.
Can I test an agent with my own scripts?
Yes. Tenant role practice runs agents against simulator scenarios with your playbook and knowledge scopes.
Is my data used to train agents for others?
Per-tenant adapters use only your opted-in data, and you can exclude your tenant from all aggregate signals.
See Training Gym & Simulation in your workflow
Tell us about your team and the outcomes you want. We will walk you through the apps, the Digital Employees and the plan for your industry.