Direct Answer (TL;DR)
Brilo AI’s Multiple Agent Strategy uses two or more Brilo AI voice agents running specialized roles (for example: triage, verification, and escalation) instead of a single generalist agent. This approach improves routing accuracy, reduces caller friction during peak volume, and lets each agent enforce narrower guardrails like intent-specific prompts and confidence thresholds. Multiple agents also enable parallel handling (concurrency), clearer escalation paths, and faster warm transfers to humans when needed. Use this strategy when you need predictable behavior for regulated scenarios, better call triage, or clearer analytics across business functions.
Why choose multiple agents over one?
Multiple Brilo AI voice agents let you split responsibilities (triage vs. transactional) so each agent is simpler to tune and to audit.
Is this the same as call routing?
It complements intelligent call routing: routing decides which Brilo AI voice agent to start, and the chosen agent runs a focused flow.
Will multiple agents increase cost?
It can add configuration effort up front but typically reduces human transfers and time-to-resolution by keeping flows focused and measurable.
Why This Question Comes Up (problem context)
Enterprises ask this because a single, catch‑all Brilo AI voice agent often becomes a maintenance bottleneck. Large contact centers in healthcare and banking face competing needs: triage, authentication, compliance checks, and complex escalations. Buyers want predictable behavior for high-risk calls, clear routing for specialized teams, and measurable quality across domains. Multiple agents let organizations isolate risk, simplify testing, and scale different conversational skills independently.
How It Works (High-Level)
Brilo AI implements a Multiple Agent Strategy by letting you define separate voice agents and routing rules that start the right agent per caller context (for example, language, intent, or time of day). Each Brilo AI voice agent runs its own conversational flow, intent models, and handoff rules; intelligent call routing then forwards callers to the correct agent or to a human. This separation reduces cross‑intent ambiguity and makes confidence scores easier to manage across different call types. For more on how Brilo AI detects caller intent and routes to agent workflows, see the Brilo AI article on how the AI understands caller intent: How Brilo AI understands what the caller wants.
In Brilo AI, agent pool is a named set of voice agents configured to serve related call types and availability rules.
In Brilo AI, confidence score is the runtime metric the system uses to decide if an agent should continue, retry, or trigger an escalation.
In Brilo AI, session context is the transient data (transcript, entities, intent) passed between agents or to humans to maintain continuity.
Related technical terms: intelligent call routing, call triage, concurrency, confidence score, warm transfer, intent detection.
Guardrails & Boundaries
Use focused guardrails when you deploy multiple Brilo AI voice agents. Limit each agent’s scope (for example, “appointment triage only” or “claim status lookup only”) and configure escalation thresholds so the agent does not attempt tasks outside its scope. Apply confidence score cutoffs and explicit “out-of-scope” prompts to trigger warm transfer or callback to a human. Do not use a Multiple Agent Strategy to bypass human review in regulated interactions; instead, use it to create clearer escalation points.
In Brilo AI, escalation threshold is the configured confidence level or rule set that forces the agent to hand off to a human or alternate flow. For details on maintaining naturalness and safe handoffs, review Brilo AI’s guidance on voice behavior and handoffs: Does the AI sound natural or robotic? (handoff & configuration guidance).
Applied Examples
Healthcare: Use three Brilo AI voice agents—one for appointment triage (symptom screening), one for patient identity verification, and one for billing inquiries. The triage agent routes urgent symptoms to a nurse line; the verification agent only handles authentication prompts and escalates low-confidence identity checks to human staff. This reduces repeated questioning and preserves clinical staff time.
Banking: Deploy separate Brilo AI voice agents for balance enquiries, payment disputes, and fraud screening. The fraud screening agent has stricter guardrails and immediate warm-transfer rules when it detects phrases indicating compromise, while the payments agent focuses on transaction lookups.
Insurance: Use one Brilo AI voice agent for new claim intake (collects key entities), another for policy status checks, and a third for agent-assisted negotiations; each agent logs structured entities that speed human follow-up.
Note: Examples illustrate workflows and configuration patterns. Do not treat these examples as legal or compliance advice.
Human Handoff & Escalation
Brilo AI supports warm transfers (handoff with context) and cold transfers. With multiple agents you can design handoffs between agents or from an agent to a human: pass the session context (transcript excerpt, extracted entities, detected intent, and confidence score) to avoid repetition. Configure routing rules so that if Agent A hits its escalation threshold it either starts Agent B (for a different specialized task) or initiates a warm transfer to live staff. Use availability rules and capacity checks to avoid routing callers to an understaffed human queue.
Typical escalation triggers in Brilo AI include: explicit caller request for a human, low confidence on required fields, sensitive keywords, or sentiment signals that indicate frustration.
Setup Requirements
Identify the call scenarios you want separated (triage, verification, billing, fraud) and document the expected interaction for each role.
Create distinct Brilo AI voice agents in the console for each role and author focused flows and prompts for each agent.
Configure intelligent call routing rules to start the correct Brilo AI voice agent based on caller context (language, IVR selection, or intent).
Connect your CRM and webhooks so agents can look up or persist caller data (for example, your CRM and your webhook endpoint).
Define confidence score thresholds and escalation rules for each agent so transfers are predictable.
Test each agent in isolation and run end‑to‑end scenarios that include agent-to-agent and agent-to-human handoffs.
Monitor analytics and iterate on agent prompts and routing rules.
See guidance on Brilo AI agent concurrency and capacity planning for step 6: Can the AI handle multiple callers at the same time?
Business Outcomes
A Multiple Agent Strategy with Brilo AI produces clearer operational control and measurement. Realistic outcomes include fewer unnecessary human transfers, faster identification of high‑risk calls, more consistent compliance controls per call type, and cleaner analytics per business function. This approach reduces cognitive load on single agents and accelerates targeted training and improvements for each conversational domain.
FAQs
How many Brilo AI voice agents should I start with?
Start with 2–4 focused agents that match your primary pain points (for example, triage and verification). Expand incrementally after measuring call volumes and transfer patterns.
Will multiple agents complicate reporting?
No. Brilo AI captures transcripts, intent tags, and session metadata per agent so you can segment analytics by agent role and by routing rule to get clearer performance insights.
Can agents share context between them?
Yes. Brilo AI can pass session context (intent, entities, recent transcript) between agents or to human agents during warm transfers to prevent repetition and preserve continuity.
Does this increase regulatory risk?
It can reduce risk by isolating sensitive tasks to agents with stricter guardrails, but regulatory requirements depend on your organization. Configure explicit escalation and logging where regulated handling is required.
How do I keep models consistent across agents?
Keep shared entity definitions and canonical prompts in a common design document, and use Brilo AI’s testing cycle to validate behavior after each change.
Next Step
Configure a proof-of-concept: create two focused Brilo AI voice agents in your console, set routing rules, and run live tests to tune confidence thresholds and handoffs.