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Conversational AI

CogniAgent’s conversational AI transforms how you interact with customers and team members. Instead of rigid chatbots that follow scripts, you get intelligent agents that understand context, handle variations, and complete real tasks.

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What makes it different?

Not just chat

Conversations that actually accomplish work — collect data, trigger actions, update systems.

Natural dialogue

Users talk naturally. The AI understands intent, not just keywords.

Context-aware

Remembers what was said, understands corrections, handles topic changes gracefully.

Human backup

Seamless handoff to humans when needed — never leaves users stuck.

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Core capabilities

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Intelligent data collection

Turn form filling into conversations. Instead of presenting users with a list of fields, the AI:
  • Asks questions naturally based on context
  • Accepts information in any order
  • Understands variations (“$50k”, “fifty thousand”, “around 50,000”)
  • Asks for clarification only when truly needed
  • Lets users update previous answers
FieldInput
Name_______________
Company_______________
Budget_______________
Timeline_______________
[Submit]

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Knowledge-powered responses

Connect your AI to your knowledge base so it can answer questions during conversations:
  • Product information and FAQs
  • Pricing and policies
  • Technical documentation
  • Company information
The AI searches automatically when users ask questions it can’t answer from context.
Knowledge search is a tool, not a default behavior. The AI decides when to search based on the conversation — it won’t search for every message.

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Automatic escalation

The AI knows when to get help:
  • User requests — “Can I talk to a person?”
  • Frustration signals — Repeated misunderstandings, negative sentiment
  • Capability limits — Questions outside its knowledge or authority
  • Policy requirements — Certain decisions require human approval
Escalations include full conversation context so humans can pick up seamlessly.

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Architecture

CogniAgent’s conversational AI is built on three layers:

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Channels

Where conversations happen. Users interact through their preferred channel; the AI sees a unified conversation. Learn more about channels →

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Skillset Engine

The brain of conversations. Manages dialogue flow, decides what to do with each message, tracks collected data, and handles edge cases. Learn more about skillsets →

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EDAA

The action layer. When conversations trigger actions (update CRM, send notification, start process), EDAA executes them as part of your workflows.

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Building conversational experiences

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1. Define your skillset

Start by describing what information you need to collect: 
{
  "agentId": "appointment-scheduler",
  "name": "Appointment Scheduler",
  "description": "Schedule appointments with customers",

  "persona": {
    "agentName": "Scheduling Assistant",
    "tone": "Professional and efficient"
  },

  "formFieldsMapping": {
    "service_type": {
      "template": {
        "id": "service_type",
        "name": "Service Type",
        "description": "What service the customer needs",
        "type": "selection",
        "options": ["Consultation", "Demo", "Support Call"],
        "required": true
      }
    },
    "preferred_time": {
      "template": {
        "id": "preferred_time",
        "name": "Preferred Time",
        "description": "When the customer wants to meet",
        "type": "text",
        "required": true
      }
    }
  }
}

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2. Connect to channels

Choose how users will reach your AI:
  • Web widget for website visitors
  • Email for inbound inquiries
  • Slack for team interactions
  • SMS for mobile-first experiences

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3. Add to your workflow

Use the Ask a Person node to start conversations:

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4. Handle the results

When conversations complete, your workflow receives structured data: 
{
  "collectedData": {
    "service_type": "Demo",
    "preferred_time": "Tuesday afternoon"
  },
  "status": "completed",
  "conversationId": "conv_abc123"
}

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Conversation flow

Every conversation follows this pattern:
1

Initialization

Workflow triggers the skillset with context (who we’re talking to, any known information).
2

Greeting

AI sends an opening message appropriate to the context and channel.
3

Collection loop

For each piece of needed information:
  • AI asks a question (or acknowledges if user already provided it)
  • User responds
  • AI decides: accept, clarify, skip, or escalate
4

Confirmation

AI summarizes collected information and asks user to confirm.
5

Completion

Conversation ends, data is passed back to the workflow for processing.

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Guardrails and safety

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Built-in protections

AI won’t generate harmful content, provide professional advice (legal, medical, financial), or engage with inappropriate requests.
Define what topics the AI should discuss. Off-topic messages get gentle redirects.
Configurable limits on clarification attempts prevent infinite loops.
Automatic triggers for human handoff when the AI can’t help.

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Customizable behavior

Control how your AI behaves through skillset configuration: 
{
  "behaviorConstraints": [
    {
      "name": "Stay on topic",
      "rule": "Only discuss appointment scheduling. Redirect other inquiries.",
      "severity": "warning"
    },
    {
      "name": "No pricing",
      "rule": "Do not discuss pricing. Direct pricing questions to sales.",
      "severity": "strict"
    }
  ],

  "settings": {
    "maxFieldAttempts": 3,
    "maxConsecutiveClarifications": 3,
    "requireConfirmation": true
  }
}

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Use cases

Goal: Qualify inbound leads before routing to salesFlow:
  1. Lead submits form or sends email
  2. AI engages to understand needs, budget, timeline
  3. Qualified leads routed to appropriate sales rep
  4. Unqualified leads receive helpful resources
Benefits: Sales team focuses on high-value conversations
Goal: Handle routine support requests automaticallyFlow:
  1. Customer contacts support via any channel
  2. AI identifies issue type and gathers details
  3. Simple issues resolved automatically (password reset, status check)
  4. Complex issues escalated with full context
Benefits: Faster resolution, 24/7 availability, consistent quality
Goal: Book appointments without back-and-forthFlow:
  1. Customer requests appointment
  2. AI checks availability and proposes times
  3. Customer confirms
  4. Calendar event created, reminders scheduled
Benefits: No scheduling ping-pong, immediate booking
Goal: Take orders through natural conversationFlow:
  1. Customer starts order conversation
  2. AI guides through product selection
  3. Collects delivery/payment preferences
  4. Confirms order and provides tracking
Benefits: Conversational commerce, higher completion rates

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Best practices

Start simple. Begin with a focused skillset (3-5 fields). Add complexity once you understand how users interact.
Write natural descriptions. The AI uses your field descriptions to ask questions. “What pizza the customer wants” becomes “What kind of pizza would you like?”
Test with real users. AI conversations are hard to predict. Test with actual users early and often.
Don’t over-constrain. Too many behavior constraints make the AI feel robotic. Trust it to handle reasonable variations.
Monitor and iterate. Review conversation logs regularly. Common failure patterns reveal opportunities for improvement.

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Next steps

Skillset Engine

Deep dive into skillset configuration and capabilities.

Channels

Connect your AI to communication channels.

Knowledge Base

Give your AI access to your business information.

Ask a Person

Use conversational AI in your workflows.