Building Apps with AI Agents

Here’s a basic description you can use in a Knowledge Base, onboarding doc, or sales collateral that outlines the four primary deployment types for AI Agents in raia:


Deployment Types of AI Agents in raia

The raia platform supports multiple AI Agent deployment types depending on your business goal — whether you're answering questions, qualifying leads, automating actions, or handling conversations across multiple channels.

Here are the four main ways to deploy AI Agents:


Purpose: General-purpose, multi-channel agents that respond to user questions via Live Chat, SMS, Email, or Voice.

Use Cases: Assistants.

Deployment:

  • Built in Launch Pad

  • Trained on documents, Packs, or instructions

  • Tested in Copilot


Purpose: Internal or external-facing agents trained specifically on company documentation to answer policy, process, or technical questions.

Use Cases:

  • Internal IT/support team help

  • Employee self-service tools

  • Customer-facing knowledge base automation

Key Features:

  • Integrated with raia Academy for AI-ready document ingestion

  • Deployed through Copilot (internal) or Live Chat (external)

  • Supports scoring, escalation, and ticketing integrations


Purpose: Engage prospects, ask qualifying questions, provide product information, and escalate hot leads to human sales reps.

Use Cases:

  • SDR follow-up

  • Website chat-to-lead conversion

  • Lead re-engagement via SMS/email

Key Features:

  • Can run via outbound campaigns or triggered from CRM

  • Uses Scoring Skill to assess lead quality

  • Alerts reps via Notification Skill

  • Logs conversations via Webhook Skill


4. Autonomous AI Agents (Workflow Embedded)

Purpose: Agents embedded inside automated workflows to complete background tasks without human input.

Use Cases:

  • Auto-tagging support conversations

  • Summarizing and scoring chats

  • Processing incoming form data

  • Triggering downstream actions via webhooks

Deployment:

  • Can be triggered via functions, webhooks, or internal workflow orchestration

  • Often used for backend automation, not end-user interaction

  • Supports function chaining, document processing, and API calls

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