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How to Configure Multi-Agent AI for WhatsApp Automation in BotSailor: Part-02

  • 28 Sep 2026
  • 5 min read
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TL;DR:

Part 01 prepared and tested the four specialist AI agents. Part 02 activates those agents inside AI Configuration, connects customer intents to the correct specialist, preserves recent context, and defines human-assignment behavior. Part 03 will validate the completed system through the practical WhatsApp journey.

A Practical Guide to Building Specialized AI Agents, Conversation Routing, Contextual Memory, and Human Handoff


Naming Note

The suffix “SG” is used only for naming convenience and easy identification during this demonstration. It has no special technical meaning in BotSailor. Sales Agent SG, Order Agent SG, Lead Qualification Agent SG, and Support Agent SG are simply the names used in this guide.

Guide Scope

This guide reflects the BotSailor Multi-Agent AI interface and settings shown in the included screenshots and uses Promotute Restaurant as a practical demonstration. It explains how to configure and coordinate AI Agents in BotSailor; it does not teach how to create or train a new AI model from scratch.




Reader Journey: Part 01 Build the Foundation → Part 02 Connect, Route, and Control the Agents → Part 03 See the Multi-Agent System in Action


Part 02 of 03: Connect, Route, and Control the Agents

Prepared Agents → AI Configuration → Enable AI Agent → Select Active Agents → Add Routing Rules → Configure Response, Memory & Assignment → Save Settings → Test


Step 1: How Do You Open AI Configuration?

Go to Chatbot Manager > Bots/Accounts > select Promotute > AI > AI Configuration. Turn on AI Agent so the configured specialists can start responding with their training data.

Step 2: Which AI Agents Should Be Active?

Under Active Agents, select the four specialists prepared for the bot: Sales Agent SG, Order Agent SG, Lead Qualification Agent SG, and Support Agent SG. Only selected Active Agents can participate in routing and agent-to-agent transfers.

Step 3: How Do You Configure Agent Routing?

Use Agent Routing to describe which type of customer message should start with each specialist. Click Add Rule and create one rule for each agent.

Message Type / When to Use This Agent

Assign Agent

Menu, price, food, recommendation, burger, pizza, reservation, book table

Sales Agent SG

Order status, track order, where is my order, cancel order, delivery status

Order Agent SG

Group booking, catering, event, corporate dinner, large reservation, quotation

Lead Qualification Agent SG

Refund, wrong item, missing item, payment issue, complaint, duplicate charge, support

Support Agent SG

AI Configuration showing four Active Agents and four routing rules

Step 4: Which AI Response Mode Should You Use?

For this setup, keep AI Agent for All Queries enabled so incoming customer messages are handled through the active-agent routing rules. Keep AI as a Fallback Only disabled when AI Agents are the primary response layer.

Customer Message → AI Agent for All Queries → Routing Rule → Correct Specialist Agent

Step 5: How Does Contextual Memory Work?

Enable Contextual Memory so recent conversation details remain available when a customer moves between specialists. In this example, the Memory Window is set to 10 conversations. Run a contextual memory test by sharing a detail with one agent, transferring the conversation, and confirming that the next agent can use the relevant recent context without asking the customer to repeat it.

Customer shares information → Contextual Memory keeps recent history → Next AI Agent receives context → Customer does not repeat details

Step 6: How Does Human Assignment Work?

Enable Assign to Team or Department when a conversation may need human support. Select the appropriate team and write a short customer-facing Assignment Message. This creates a real-time agent assist path: AI handles routine work, while a person can take over when the request needs human judgment or the customer explicitly asks for support.

AI cannot resolve issue / customer asks for a person → Assign to Team or Department → Assignment message sent → Human team handles the case

Example Assignment Message

Your conversation has been assigned to our team. We will get back to you shortly.

Step 7: Which Misc. Settings Should You Enable?

Use these controls to refine the live conversation experience. Enable business-off-hour assignment protection when needed and enable the typing indicator for a more natural WhatsApp experience.

Business-hour rule ON → Assignment follows working hours | Typing Indicator ON → Customer sees AI is preparing a response

Step 8: How Do Behavior & Restrictions Work?

Add Restricted Topics only for subjects the AI should not handle. Define a clear response that is returned when a restricted topic is detected.

Customer enters restricted topic → BotSailor checks Restricted Topics → AI avoids answering → Restriction response is sent

Example Restriction Response

Sorry, I cannot assist with this query. Please contact support.

Step 9: How Do Business Hours Affect Human Handoff?

Set the correct time zone and working hours for the human team. In this example, the AI Configuration uses Asia/Dhaka with 09:00 AM to 06:00 PM. Human assignment should follow the actual support schedule used by the business.

Customer requests human support → Check Time Zone + Business Hours → Inside hours: assign human → Outside hours: AI continues helping

Step 10: How Should You Configure Advanced Settings?

Set the AI reply delay and reasoning level. In this example, Delay for AI Reply is 0 seconds and AI Reasoning Level is Medium.

Customer message → Reply Delay → AI processes request at selected Reasoning Level → Response is sent

Response Mode, Context & Memory, Assignment, Misc. Settings, Restrictions, Business Hours, and Advanced Settings

Response Mode, Context & Memory, Assignment, Misc. Settings, Restrictions, Business Hours, and Advanced Settings

Step 11: How Do You Test the Multi-Agent Configuration?

Click Save Settings after reviewing the configuration. Then test different customer messages to confirm that each intent reaches the correct specialist.

Test Message

Expected Agent

How much is the Chicken Burger?

Sales Agent SG

Where is my order?

Order Agent SG

I need catering for 30 people.

Lead Qualification Agent SG

I received the wrong item and want a refund.

Support Agent SG

After basic routing works, test agent-to-agent transfers, reservation flows, order actions, and human handoff separately.


Continue to Part 03: See the Multi-Agent System in Action

The specialist agents are now prepared, activated, and connected through routing rules. BotSailor knows which agent should handle each type of customer request and how conversations can move between specialists.

Part 03 brings everything together in a complete WhatsApp journey covering Sales, Lead Qualification, the reservation flow, Order, Support, and human handoff.


Part 02: Configure the System → Part 03: (See the Multi-Agent System in Action): Test the Complete Customer Journey


Siddhartha Ghosh

Siddhartha Ghosh

FAQ

Frequently Asked Questions

Find answers to common questions about this topic

A contextual memory test checks whether relevant recent conversation details remain available when a customer moves between agents. BotSailor’s Context & Memory settings help reduce the need for customers to repeat information. The broader keyword remote memory may also relate to maintaining information beyond the immediate response context.

For how is agentic AI different from earlier forms of AI, the key difference is action. Traditional AI mainly generates responses, while agentic AI can follow instructions, use tools, call workflows, save data, transfer conversations, and interact with business systems.

AI agents for ecommerce can handle product questions, order tracking, refunds, lead qualification, and customer support. The same multi-agent architecture shown in this restaurant example can be adapted to ecommerce workflows.

For businesses researching how to automate your business, start by separating repetitive processes into specialist roles. Then configure each agent with a System Prompt, Knowledge Source, required actions, routing rules, memory, and human handoff.

This is a practical guide to building agents, not a machine-learning guide on how to create an AI model or how to build an AI model from scratch. It explains how to configure existing AI capabilities in BotSailor into specialized WhatsApp AI Agents, connect knowledge and actions, and make the agents work together.