Direct Answer (TL;DR)
Yes. Brilo AI provides a test module (evals) that simulates real conversations so teams can verify a draft Brilo AI voice agent before publishing. The AI agent for phone calls runs the best automated simulations, captures recordings and transcripts, and surfaces pass/fail results and tags so reviewers can iterate before deployment.
Why This Question Comes Up (problem context)
Engineering and operations teams must avoid regressions, reduce hallucinations, and validate routing and handoff behavior before callers reach production. Buyers ask whether Brilo AI voice agent capabilities offer a reproducible way to exercise common intents, edge cases, and number-based routing without impacting real customers.
How It Works (High-Level)
The Brilo AI test module (evals) runs scenarios against a selected Brilo AI voice agent. Test scenarios come from user-provided prompts and variations. An agent-based simulation mode runs dozens of simulated calls automatically. A manual mode lets testers step through paths one by one. Each simulated call produces an audio recording, a transcript, and metadata such as tags, confidence scores, and pass/fail status. The AI phone call agent testing pipeline connects the best routing rules and phone number assignments when scenarios require number-based behavior.
Guardrails & Boundaries
Brilo AI voice agent testing enforces safety and configuration boundaries. Typical guardrails include confidence-based escalation rules, restricted topics that must trigger an automatic handoff, and recording consent settings. The Brilo AI voice agent must not guess required data when confidence is low. When the Brilo AI voice agent hits a defined boundary the test workflow should route to an escalation path or mark the scenario as a fail for remediation. Recording settings and data handling policies must be confirmed before running tests that use live numbers.
Applied Examples
A support team uses the Brilo AI test module to validate billing intent handling across 25 utterance variations and to confirm the correct queue is selected for escalation.
A 24/7 customer service team runs an agent-based simulation to confirm lead qualification prompts capture company size and timeline before transferring to a human rep.
An operations team tests SSML voice variations and phonetic lexicon changes through targeted simulations to measure caller naturalness and transcription quality.
Human Handoff & Escalation
Human handoff means transferring the call from the Brilo AI phone call agent to the best human agent available. The Brilo AI voice agent testing features validate handoff triggers and the quality of the handoff summary. A handoff can include a short intent summary, captured slot values, and tags for routing. The Brilo AI voice agent should execute configured escalation rules when confidence thresholds are not met or when a caller asks for a person. Test scenarios should include explicit handoff cases so reviewers can confirm expected behavior and context transfer.
Setup Requirements
To run tests, buyers typically provide the following:
A draft Brilo AI voice agent configured in the platform.
Test permissions for the team to access the Test module (evals).
Representative prompts and conversational variations for each scenario.
Acceptance criteria that define pass/fail conditions for transcripts, tags, and confidence.
Routing and phone number assignments when scenarios must exercise number-based behavior.
Recording and data retention settings that meet organizational privacy policies.
For guidance on voice quality and recording settings, review Brilo AI documentation on naturalness and recording configuration. For guidance on test design and running controlled simulations, review Brilo AI resources about delivering 24/7 voice coverage and test best practices.
Business Outcomes
Controlled testing with the Brilo AI test module reduces deployment risk and lowers post-release issue volume. The Brilo AI voice agent testing features increase confidence in routing natural conversations and handoff behavior. Recording and transcript artifacts provide audit evidence for QA and help remediation cycles run faster. Tuning the Brilo AI voice agent before publish improves first call resolution for routine requests and preserves human agents for complex conversations.
Next Step
Run a controlled test group in the Brilo AI dashboard using representative prompts and defined pass/fail criteria. Start with a small set of scenarios, confirm recording and routing settings, and expand coverage once results are stable. For assistance in implementing the best testing methods for your AI phone call agent, book a call with us today.