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AI Chatbots

Kaanha AI includes a powerful chatbot system with two types: AI Agents (LLM-powered) and Flow Bots (visual decision trees). Both can run simultaneously and hand off to human agents.

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AI Agents

AI Agents use large language models to have natural conversations with your customers. They are configured with a business task so they stay focused on your use case.

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Supported Agent Types

TypeDescriptionBest For
Built-in LLMOpenAI, Anthropic, Google, Groq, or any OpenAI-compatible endpointGeneral FAQ, support, lead qualification
Python Bot WebhookYour own chatbot endpoint (HMAC-signed POST)Custom logic, existing bots
Notion Knowledge BaseRAG over your Notion pagesFAQ from docs, product catalogs

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Creating an AI Agent

  1. Go to Chatbot → AI Agents → Create Agent
  2. Fill in the required fields:
FieldDescription
NameInternal name for the agent
Business TaskRequired — what this agent does (FAQ, Order Tracking, Support, Booking, Lead Qualification, Feedback)
Trigger KeywordsComma-separated words that activate this agent (e.g., help, support, hi)
System PromptInstructions for the AI about your business, tone, and what to answer
ProviderOpenAI / Anthropic / Google / Groq / Custom LLM
Modele.g., gpt-4o-mini, claude-haiku-4-5, gemini-1.5-flash
  1. (Optional) Configure advanced settings:
    • Max Turns: Auto-handoff to human after N messages (default: 10)
    • Business Hours Only: Agent only responds during your configured business hours
    • Temperature: Response creativity (0.0–1.0)
    • Auto-inject Product Catalog: Prepend your active product catalog into the system prompt on every turn

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Business Task Requirement

Every AI Agent must have a businessTask field. This is required by Meta’s January 2026 AI Policy. The platform appends a safety prompt enforcing task boundaries to every agent’s system prompt. Available tasks: faq, order_tracking, customer_support, booking, lead_qualification, feedback, other

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Testing an AI Agent

Use the Test button on the agent card to simulate a conversation. The test endpoint also returns a diagnostics.delegationPath field showing which sub-agents were invoked, making it easy to debug multi-agent setups.

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Knowledge Bases

Knowledge Bases let you ground AI Agents in your own content. An agent can be linked to multiple knowledge bases; retrieval is hybrid (vector + keyword) across all linked bases.

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Source Types

SourceHow to add
Notion pages / databasesConnect your Notion workspace in Settings → Integrations, then select a database in the KB editor. Uses your org-level Notion OAuth token — no separate API key needed.
File uploadPDF, DOCX, CSV, TXT — up to 50MB per file
Manual FAQEnter Q&A pairs directly

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CRM-Backed Knowledge Bases

Set a KB’s Entity field to contact, deal, task, product, company, or supplier (or a custom entity slug). The KB is automatically re-indexed whenever that entity type is mutated — imports, webhook events, AI tool updates all trigger a re-sync. This keeps the KB current without any manual sync step.

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Contact Memory

AI Agents automatically extract facts from closed conversations and remember them for the next interaction. Memory is per-contact and surfaced in the system prompt at the start of each turn. Memory entries include: name, preferences, past issues, purchase history, language — whatever the LLM extracts as meaningful. Entries have a confidence score and an optional expiry date.

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Product Catalog Auto-Injection

Enable Auto-inject Catalog on an agent to automatically prepend your top-N active products into the system prompt. The agent can then answer pricing, availability, and product questions without any tool calls. Configure the number of products (default: 20) in the agent settings. The catalog block is capped at 3,000 characters and placed before the agent’s own system prompt.

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Built-in Tools

AI Agents can invoke built-in CRM and product tools mid-conversation without any configuration:
ToolWhat it does
check_product_availabilityChecks if a product is in stock
search_productsFull-text search across your product catalog
get_product_detailsReturns product name, price, SKU, stock count
check_stockReturns current stock quantity
get_priceReturns price for a product
business_hoursReturns whether the org is currently open
schedule_callbackCreates a callback task for a contact
get_contact_infoReturns the current contact’s profile
create_support_ticketOpens a support ticket from within chat
get_deal_detailsReturns deal + line items for the contact
get_recent_dealsReturns recent deals from the CRM
get_recent_notesReturns recent contact notes
get_recent_callsReturns recent voice call transcripts
find_similar_past_casesRAG-powered: finds similar past conversations
get_contact_profileFull contact profile including linked company
Enable or disable specific tools from the agent’s Tools tab. The toggle is opt-in — new agents start with no tools enabled.

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Multi-Agent Orchestration

Feature flag required. Multi-agent orchestration is gated behind the multiAgent feature flag. Contact your admin to enable it for your organization.

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Overview

A Lead Agent can delegate work to one or more Helper agents mid-conversation. The caller (contact) sees one seamless conversation; the delegation is invisible to them.
  • Lead Agent — handles the primary conversation, decides when to delegate
  • Helper — a specialist agent called as a tool by the Lead
  • Solo — a standalone agent with no delegation

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Setting Up an Orchestration

  1. Go to Chatbot → Orchestrations
  2. Click + New Orchestration to create a Lead Agent
  3. On the orchestration board, drag-and-drop Helper agents onto the canvas
  4. Connect them to the Lead Agent
  5. Each Helper needs a short description (used by the LLM to decide when to call it)
  6. Save
The Lead Agent sees each Helper as a callable tool. When the LLM decides to delegate, it calls the Helper’s full LLM + tool loop and returns the result.

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Depth Limits

ModeMax delegation depth
Text chat3 levels
Voice2 levels

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Human Handoff Through Chains

If any Helper agent triggers a HUMAN handoff (keyword or tool call), the signal propagates up through the entire delegation chain and routes the conversation to a human queue immediately.

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Testing Orchestrations

The Test button on a Lead Agent card runs a simulated conversation and returns diagnostics.delegationPath — a trace of which agents were called and in what order.

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Flow Bots

Flow Bots are visual, rule-based conversation trees. They’re deterministic — the same input always produces the same output.

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Creating a Flow Bot

  1. Go to Chatbot → Flow Bots → Create Flow
  2. Enter a name and trigger keywords
  3. Click Visual Editor → to open the drag-and-drop canvas

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Node Types

NodeDescription
MessageSend a text/media message to the contact
ConditionBranch based on contact data, tags, or message content
DelayWait N minutes/hours before continuing
WebhookCall an external URL and branch on response

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Priority Chain

When an incoming message arrives, Kaanha AI checks in this order: 
1. Is there an active AI Agent conversation? → Continue it
2. Is there an active Flow Bot conversation? → Continue it
3. Does any AI Agent keyword match? → Start AI Agent
4. Does any Flow Bot keyword match? → Start Flow Bot
5. No match → Place in human queue (OPEN status)

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Human Handoff

AI Agents automatically hand off to humans when:
  • The contact types HUMAN, AGENT, HELP, or any of 12 configured handoff keywords
  • The agent’s [HANDOFF] signal appears in the response
  • Max turns are reached
  • A content safety violation is detected
After handoff, the conversation status changes to OPEN and appears in the agent queue.

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Content Safety

All AI responses pass through a content safety filter (22 patterns, 7 categories) before being sent:
  • Harmful instructions
  • Personal data requests
  • Off-topic content
  • Prompt injection attempts
Blocked responses trigger automatic handoff to a human agent.

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AI Disclosure

Per Meta’s January 2026 policy, the first AI response in every new conversation includes a mandatory disclosure that the contact is speaking with an AI. This is enforced platform-wide and cannot be disabled.

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Chatbot Analytics

Go to Chatbot → Analytics to see:
  • Total conversations handled per agent
  • Handoff rate
  • Average turns before handoff
  • Most common trigger keywords
  • Token usage per agent