Multi-Bot Conversations

Multi-bot conversations allow multiple Aimogen chatbots to participate within the same conversational environment, each with its own persona, model, and behavior. Instead of a single assistant responding to the user, multiple bots can contribute, reply in sequence, or interact based on defined rules.

This feature is intentional, controlled, and opt-in. Chatbots do not talk to each other by default.

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What Multi-Bot Conversations Are #

In a multi-bot setup:

• more than one chatbot exists on the site
• each chatbot has its own persona and configuration
• multiple bots can respond within the same conversation flow

Each bot remains independent. There is no shared “brain” or merged personality.

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How Bots Participate Together #

Multi-bot conversations are typically enabled through:

• explicit configuration
• rules and workflows
• shared frontend context

Bots do not automatically respond to the same message unless configured to do so.

You decide:

• which bots are active
• when they respond
• in what order
• under what conditions

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Common Multi-Bot Patterns #

Typical setups include:

• Support + Sales
  One bot answers technical questions, another handles pricing or upgrades.
• Expert Panel
  Multiple bots with different roles (e.g. developer, marketer, analyst) respond to the same topic.
• Escalation Flow
  A general bot responds first, then escalates to a specialist bot if needed.
• Language-Based Bots
  Different bots respond based on detected language.

Each bot keeps its own persona and response style.

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One User, Multiple Bots #

From the user’s perspective:

• all bots appear within the same chat interface
• each bot is clearly identified (name, avatar)
• responses may alternate between bots

This avoids confusion and preserves conversational clarity.

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Personas Still Apply #

Each bot’s persona remains fully enforced.

This means:

• tone does not bleed between bots
• roles remain distinct
• instructions are not shared
• one bot cannot override another’s behavior

Multi-bot does not weaken persona control.

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Conversation Context Handling #

Context handling depends on configuration:

• bots may share the visible conversation history
• bots may receive only filtered context
• bots may react only to specific messages or triggers

Context sharing is not automatic. It must be explicitly enabled or designed via workflows.

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Providers and Models per Bot #

Each bot can:

• use a different AI provider
• use a different model
• have different limits and performance characteristics

This allows mixing:

• fast chat bots
• reasoning-heavy bots
• cost-optimized bots

All within the same conversation.

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Frontend vs Backend Behavior #

On the frontend:

• multi-bot conversations are user-facing
• bots are visually distinct
• rules control participation

In the backend (Playground):

• multi-bot setups are used mainly for testing
• useful for validating interaction logic
• not intended as a production simulation

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Limits, Logging, and Cost #

Each bot:

• consumes its own API usage
• respects its own limits
• generates its own logs

A single user message triggering multiple bots will result in multiple API calls.

This should be considered when designing high-traffic setups.

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What Multi-Bot Conversations Are Not #

They are not:

• autonomous agent swarms
• self-organizing bot networks
• bots debating each other without rules
• shared-memory AI systems
• model fine-tuning mechanisms

They are coordinated chatbot interactions, not emergent AI behavior.

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When to Use Multi-Bot Conversations #

They make sense when:

• roles must be clearly separated
• answers benefit from multiple perspectives
• escalation or specialization is needed
• one bot should not handle everything

They are unnecessary for simple Q&A bots.

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Best Practices #

• keep bot roles clearly distinct
• limit the number of bots responding at once
• avoid overlapping responsibilities
• test interaction order carefully
• monitor usage costs

More bots does not automatically mean better answers.

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Summary #

Multi-bot conversations allow multiple Aimogen chatbots to participate in a shared conversational flow, each with its own persona, model, and rules. Bots remain independent and only interact when explicitly configured to do so. Used correctly, this enables advanced support, escalation, and expert-style chat experiences while maintaining strict control over behavior, cost, and clarity.