Brilo AI’s Governance Model is a layered, policy-driven approach that combines configurable guardrails, routing rules, and human-in-the-loop escalation to keep AI voice agent behavior predictable and auditable. It uses explicit limits (session limits), confidence thresholds for intent detection, and deployment-level controls (telephony provisioning and account caps) to stop unsafe actions and ensure clear handoffs. The model is implemented through Brilo AI configuration, per-workflow personas and prompts, and operational monitoring that surfaces exceptions to your support or compliance teams. This balances autonomous handling with required human escalation so regulated organizations can delegate routine calls while keeping final control.

What is another way to ask this?

Enterprise buyers ask about governance because AI voice agents touch regulated interactions, sensitive data, and large call volumes. Decision-makers need to know who owns decisions, how escalation works, and what controls exist to prevent unauthorized or non‑compliant actions. For healthcare and financial services organizations, the ability to audit behavior and force human review is a primary procurement requirement. Brilo AI’s Governance Model answers those concerns by making policy enforcement and escalation an explicit, configurable part of deployment.

Brilo AI applies governance at three levels: platform defaults, deployment configuration, and per-workflow rules. Platform defaults set account-wide limits (provisioning caps and telephony limits). Deployment configuration defines routing, recording, and retention behavior. Workflow rules (prompts and persona) define what the voice agent is allowed to say and do. Confidence threshold is the numeric setting that determines when the voice agent should seek clarification or trigger a handoff. Session limit is the configured maximum conversational context or duration for a single call. For guidance on consistent behavior across workflows, see the Brilo AI consistency across calls guide: Brilo AI consistency across calls guide.

Brilo AI enforces explicit guardrails so the voice agent remains within approved scope and avoids improvisation. Guardrails include allowed topics, disallowed phrases or actions, mandatory disclosures in the opening script, confidence thresholds for intent detection, and rules that forbid initiating high‑risk operations without human authorization. An escalation trigger is the configured condition (for example, low confidence, repeated clarification failures, or specific keywords) that routes a call to a human agent. Brilo AI also supports fallback prompts that ask clarifying questions rather than guessing when confidence is low. For details on fallback and escalation behavior, see the Brilo AI fallback and escalation guide: Brilo AI fallback and escalation guide.

Brilo AI supports multiple handoff patterns: soft handoff (warm transfer), hard handoff (immediate transfer), and callback scheduling. Handoffs are driven by workflow rules: low confidence, topic out of scope, explicit caller request to speak to a human, or sensitive-data detection.

When a handoff happens, Brilo AI can pass structured context (detected intent, transcript summary, and what was already asked) to reduce repeat questions for the human agent. Administrators can configure whether recordings and transcripts are retained and which metadata accompanies the transfer.

For operational guidance on provisioning and capacity planning, refer to the Brilo AI performance and provisioning guide: Brilo AI performance and provisioning guide.

Adopting the Brilo AI Governance Model enables predictable, auditable automation while preserving human oversight where it matters. Typical outcomes include fewer misrouted high-risk calls, faster resolution for routine inquiries, and clearer accountability through retained conversation metadata and structured handoff packets. These benefits support regulated organizations that must demonstrate controlled automation without removing necessary human approvals.

What is a confidence threshold and how should we set it?

A confidence threshold is the score at which Brilo AI decides the detected intent is reliable. Start conservative for regulated workflows (higher threshold) so the agent routes more cases to humans, then lower the threshold as your transcripts and models improve through tuning.

Can Brilo AI block the agent from collecting certain data?

Yes. You configure disallowed data patterns and decline rules in the workflow prompt. When the agent detects a disallowed field, it will stop collection and follow your configured escalation or refusal path.

How are recordings and transcripts handled?

Recording and transcription behavior is configurable at deployment. You choose whether to enable call recording, how long to retain transcripts, and whether to export them to your CRM or webhook endpoint. Follow your internal data retention policy when configuring these settings.

Will Brilo AI make regulatory decisions autonomously?

No. Brilo AI’s governance model is designed to prevent autonomous execution of high‑risk or regulated decisions unless you explicitly permit them and pair them with human authorization rules.

How do I audit agent decisions?

Brilo AI provides stored transcripts, confidence scores, and routing logs for each call. These artifacts support post‑call review and compliance investigations when you need an audit trail.