Direct Answer (TL;DR)
How is AI behavior updated over time? The Brilo AI agents updates behavior through an explicit versioning workflow: business and e-commerce administrators can change the knowledge base, edit the agent prompt and settings, create a named draft version, run tests or A/B tests against Evals or a test phone number, and then publish the vetted version. Brilo AI voice agent capabilities also support rollback to prior versions if the published change negatively affects call metrics.
Why This Question Comes Up (problem context)
Operations and product teams in business and e-commerce need predictable change control for AI voice agent automation. Teams ask how Brilo AI voice agent behavior changes will reach live callers, how to validate updates safely, and how to compare variants. The concern is avoiding surprise regressions in call completion, hang-ups, or transfer rates after a change.
How It Works (High-Level)
Brilo AI voice agent updates follow a standard edit, test, and publish lifecycle. Typical steps include:
Update knowledge base content and re-run indexing or site scrape so the Brilo AI voice agent has the latest sources.
Edit prompt text and agent settings to change AI agent's voice, disclosure, or patience.
Use the console to save a draft and create a named version for evaluation.
Run tests using Evals, a Test group, or a dedicated test phone number to collect transcripts and metrics.
Optionally run an A/B test to compare two versions on transfer rate, hang-ups, or completion.
Publish the chosen version to make the Brilo AI voice agent change live.
Versioning and publish actions provide an audit trail for prompt engineering, knowledge updates, and configuration changes.
Guardrails & Boundaries
Brilo AI voice agent guardrails limit automatic or unsupervised learning. The Brilo AI voice agent does not auto-update production calls for e-commerce businesses unless administrators explicitly accept changes. Common guardrails include confidence-based escalation thresholds, restricted topic lists, and required reviewer approval before publishing. For advanced audio behavior such as SSML or voice cloning, Brilo AI voice agent settings may require a support request and legal consent. Call recording and data handling settings must comply with organizational privacy policies during testing.
Applied Examples
Product update: The product team uploads the new FAQ PDF to the Brilo AI voice agent knowledge base, re-indexes the site source, creates a draft version, and validates accuracy in Evals before publishing.
Tone change: Customer experience updates the greeting and patience in Agent settings, creates Version 2, and runs a 7-day A/B test against Version 1 to measure hang-ups and transfers.
Error remediation: Support tags failing calls, extracts transcripts, and updates the knowledge base source and prompt priorities. The Brilo AI voice agent is then tested in a controlled Test group before republishing.
Human Handoff & Escalation
Brilo AI voice agent call handling features include explicit escalation and transfer rules. The Brilo AI voice agent can escalate when confidence scores fall below configured thresholds or when a caller requests a human. During a handoff, the Brilo AI voice agent can provide a short summary of collected details to the human agent if warm transfer settings are enabled. Configure warm and cold transfer behaviors in Agent settings and validate summary content in test calls.
Setup Requirements
To update Brilo AI voice agent behavior safely, prepare these inputs:
Console access with Admin or Editor role for the target Brilo AI voice agent.
Core materials for the knowledge base such as product docs, FAQs, representative call transcripts, or source URLs for scraping.
A defined prompt or persona document for prompt engineering and answer length expectations.
Test infrastructure: a Test group, Evals workspace, or a dedicated test phone number for live validation.
Routing and escalation rules, and a plan for call tagging and transcript review.
Permissions for call recording and CRM integrations are used.
Business Outcomes
When teams follow a controlled versioning approach, Brilo AI voice agent updates deliver predictable improvements in first-contact resolution and consistency across calls. Versioned prompt engineering and A/B testing reduce regressions, lower hang-ups, and improve transfer efficiency. Maintaining indexed knowledge and a clear test-to-publish workflow creates auditable change history and faster remediation when behavior drifts.
Next Step
Run a short controlled experiment. Create a draft version in the console, test with Evals or a Test group, and, if needed, run an A/B test to compare metrics. If you need help in publishing or rollback, use the console’s Versions screen to create, publish, or revert named versions and monitor call analytics after go-live. For step-by-step assistance on updating AI agents for e-commerce and business, schedule a call today.