Conversational AI Routing Architecture

Role-based AI workforce systems need routing architecture, not only good prompts.

When a message enters from WhatsApp, Instagram, or webchat, the platform must answer five questions in order:

1. Which company owns this conversation?

2. Which channel context applies?

3. Which AI role should respond?

4. Which knowledge blocks are allowed?

5. Should this be escalated to a human?

Without this sequence, systems become generic chatbot layers.

Architecture Overview

H2: Ingress Layer

The ingress layer normalizes channel payloads into one internal event shape:

• `company_id`
• `platform`
• `customer_identifier`
• `message`
• `timestamp`

This enables one routing engine for all channels.

H2: Routing Layer

The router decides role assignment using:

• Intent class
• Conversation history
• Role availability
• Escalation policies

H2: Prompt Assembly Layer

The platform then builds a structured prompt from:

• Role identity
• Tone and behavior rules
• Company knowledge
• FAQ blocks
• Current user message

H2: Response Layer

AI output is returned, stored, and tracked for analytics.

For implementation patterns, see AI Workforce Guide for SMBs.

Deterministic Routing Rules

A practical approach:

H3: Sales Role Triggers

• pricing
• demo
• package
• buy
• compare plans

H3: Support Role Triggers

• issue
• problem
• error
• not working
• refund status

H3: HR Role Triggers

• apply
• job
• interview
• resume
• hiring

If confidence is low, ask a qualification question before assignment.

Stateful Conversation Context

Routing should be stateful, not stateless.

If a user was already in Support AI flow and asks "what is the update," keep support context unless clear intent shift exists.

Required state fields:

• `active_role`
• `last_intent`
• `escalation_count`
• `last_response_time_ms`

Escalation Architecture

Do not rely on "AI decides if unsure."

Use explicit escalation conditions:

• Sensitive billing dispute
• Legal or compliance request
• Threat language
• Repeated unresolved interactions

Escalation packet should include:

• Conversation excerpt
• Assigned role
• Reason code
• Recommended next action

Real-Time Considerations

To preserve user trust, support:

• Typing indicators
• Streaming response tokens
• Channel status tracking
• Message persistence in the same transaction window

This improves perceived speed and operational traceability.

Data Model Requirements

At minimum:

• `ai_agents`
• `agent_channels`
• `agent_training_profiles`
• `conversations`
• `integrations`
• `audit_logs`

Use company-scoped keys and indexed lookup paths for response speed at scale.

Error Handling Strategy

H2: Retryable Errors

• Upstream timeout
• transient network failure
• token refresh race

H2: Non-Retryable Errors

• invalid credentials
• disabled subscription
• policy denial

For non-retryable cases, send a safe fallback and log diagnostics.

Performance Targets

Recommended starting targets:

• Router decision: under 50 ms
• Prompt assembly: under 80 ms
• AI first token: under 1.5 s
• Final response: under 4 s for normal requests

Performance should be measured by role and channel, not global average only.

Security Controls for Routing

• Validate company headers on every request
• Prevent cross-company message reads
• Encrypt integration tokens
• Verify origin and signatures for external events

Review AI Workforce Security and Compliance Checklist for full control coverage.

Final Recommendation

The most reliable AI workforce architecture is deterministic routing plus strict role boundaries.

Treat routing as a core product subsystem, not middleware glue.

CTA

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