Output Settings - Meetzy Docs

What are Output Settings?

Output Settings define what information your agent should extract and capture from each conversation. This data is then available via webhooks or API for use in your CRM, analytics, or other systems.

Well-configured output settings turn every call into structured, actionable data.

Success Evaluation

Beyond extracting custom data points, you can configure your agent to automatically evaluate whether each call achieved its intended goal. This is done through the Success toggle and its associated Success Criteria field.

The Success Toggle

When enabled, the AI evaluates every completed call against your defined criteria and returns a success boolean (true/false) alongside your other output data.

Enable Success Tracking

Toggle on Success in the Output Settings panel to activate call outcome evaluation.

Define Success Criteria

Once enabled, a Success Criteria text field appears. Describe in natural language what makes a call successful for your specific use case.

AI Evaluates Automatically

After each call, the AI reads the full conversation and determines whether the criteria were met.

Access the Result

The resulting success field is included in your webhook payload and API responses, ready for reporting or automation.

Writing Effective Success Criteria

The Success Criteria field accepts free-form natural language, so you can be as specific as your use case requires. The clearer the description, the more accurate the AI’s evaluation will be.

Agent Type	Example Success Criteria
Sales Agent	”The call is successful if the customer agrees to schedule a demo or provides a budget range.”
Support Agent	”The call is successful if the customer’s issue was resolved during the conversation without needing escalation.”
Booking Agent	”The call is successful if an appointment date and time were confirmed with the customer.”
Survey Agent	”The call is successful if the customer completed all survey questions.”

Write success criteria the same way you would explain it to a new team member — describe the specific outcome you’re looking for, not just the topic of the call.

Vague success criteria (e.g. “the call went well”) produce inconsistent evaluations. Always tie success to a concrete, observable outcome in the conversation.

AI Copilot Smart Suggestions

The AI Copilot provides intelligent assistance for configuring output settings with advanced change management capabilities. It analyzes your agent’s purpose and conversation goals to recommend optimal data extraction fields.

How AI Suggestions Work

Analyze Agent Context

The AI examines your agent’s role, system prompt, and knowledge base to understand its purpose.

Generate Smart Suggestions

Based on the analysis, it suggests relevant output parameters and enrichment settings tailored to your use case.

Review Granular Changes

You can review, modify, or selectively accept/reject individual parameter suggestions with one click.

Apply Changes

Accept all suggestions at once or choose specific ones that fit your needs.

AI suggestions adapt to different agent types - sales agents get lead qualification fields, support agents get issue tracking fields, and scheduling agents get appointment-related outputs.

Example AI Suggestions by Agent Type

Agent Type	Suggested Parameters	Description
Sales Agent	`lead_score`, `budget_range`, `decision_timeline`, `pain_points`	Qualify leads and track sales progress
Support Agent	`issue_type`, `resolution_status`, `satisfaction_score`, `follow_up_needed`	Track support tickets and outcomes
Booking Agent	`appointment_date`, `service_type`, `confirmation_sent`, `preferred_time`	Manage appointment scheduling
Survey Agent	`survey_responses`, `feedback_rating`, `follow_up_needed`, `completion_rate`	Collect structured feedback

Copilot Change Management

The AI Copilot features an advanced change management system that allows you to review and control all modifications before they’re applied. Pending changes are surfaced through dedicated banners so you always know what’s about to change before it happens.

Pending Changes Banners

There are two independent pending changes banners: one for Output Parameters and one for Output Enrichment. Each tracks its own set of suggestions, so you can review and act on them separately.

Output Parameters Banner
Output Enrichment Banner

Appears above the Output Parameters section whenever the Copilot proposes adding, removing, or modifying custom parameters.

Shows a count badge (e.g. “3 parameter changes”)
Lists each pending change with an Add, Remove, or Modify label
Displays the parameter name and data type for quick scanning
Includes Accept and Reject buttons to apply or dismiss all changes in the batch

Because the two banners are independent, you can accept the AI’s Output Parameters suggestions while still reviewing the Output Enrichment recommendations at your own pace (or vice versa).

Managing Pending Changes

Review Changes
Granular Control
Change Types

Each pending change shows:

Operation Type: Add, Remove, or Modify
Parameter Name: The field being changed
Data Type: String, number, or boolean
Description: What the parameter extracts

Change Operation Labels

Operation	Color	Description
Add	Green	New parameter to be created
Remove	Red	Existing parameter to be deleted
Modify	Blue	Changes to existing parameter

Review all pending changes carefully before accepting. Once applied via the Accept button, changes will update your agent’s output configuration immediately. Use Reject to discard the suggestions without modifying your current setup.

Output Parameters with Templates

Output Parameters are custom fields that your agent extracts from the conversation, now with enhanced template support.

Parameter Templates System

The new template system provides quick access to common parameter configurations:

Access Templates

Click the “Templates” button in Output Settings to see available parameter templates.

Browse Categories

Templates are organized by common use cases like customer information, lead qualification, and UTM tracking.

Add Templates

Click “Add” next to any template to include it in your output parameters.

Customize After Adding

Modify template parameters after adding them to match your specific needs.

Available Parameter Templates

Customer Information
Lead Qualification
UTM Tracking

firstname, lastname - Customer name fields
email, phone - Contact information
company, position - Business details
address, city, state, zip - Location data

lead_score (number) - Qualification score 1-10
lead_status (string) - New, Qualified, Disqualified
budget (number) - Mentioned budget amount
decision_maker (boolean) - Is decision maker
pain_points (string) - Customer challenges

utm_source, utm_medium - Traffic source tracking
utm_campaign, utm_content - Campaign tracking
utm_term - Keyword tracking

Creating Custom Parameters

Add Parameter

Click “Add Parameter” to create a new custom field.

Configure Properties

Set name, type, and description for accurate extraction.

Use AI Assistance

Let the Copilot suggest optimal parameters based on your agent’s purpose.

Test Extraction

Use the Playground to verify the parameter extracts correctly.

Parameter Properties

Property	Description	Example
Name	Field identifier (no spaces)	`customer_interest`
Type	Data type	string, number, boolean
Description	Clear extraction instruction	”Product the customer is interested in”

Advanced Text Mode with Syntax Highlighting

The enhanced text mode now includes full syntax highlighting and error detection for JSON editing.

Text Mode Features

Text Mode now provides a rich editing experience with syntax highlighting, bracket matching, and real-time error detection for JSON parameter configuration.

Using Text Mode

Switch to Text Mode

Toggle the “Text” switch to enter advanced JSON editing mode.

Edit with Syntax Highlighting

Benefit from color-coded JSON syntax and automatic formatting.

Real-time Error Detection

See validation errors immediately as you type.

Auto-format

JSON is automatically formatted for readability.

Example JSON Configuration

[
  {
    "name": "customer_interest",
    "type": "string",
    "description": "Product or service the customer is interested in"
  },
  {
    "name": "appointment_scheduled",
    "type": "boolean", 
    "description": "Whether an appointment was successfully scheduled"
  },
  {
    "name": "satisfaction_score",
    "type": "number",
    "description": "Customer satisfaction rating from 1-5"
  }
]

Text Mode is ideal for copying configurations between agents, bulk editing parameters, or importing from external sources.

AI-Enhanced Output Enrichment

The AI Copilot now provides intelligent enrichment recommendations based on your agent’s specific use case.

Smart Enrichment Suggestions

The AI analyzes your agent’s purpose, industry, and conversation goals to recommend which enrichment fields will provide the most value for your downstream processing needs.

Enrichment Change Management

Just like output parameters, enrichment changes are managed through their own dedicated pending changes banner:

Review suggested enrichment configurations
See which fields the AI recommends enabling/disabling
Accept or reject enrichment suggestions with the banner’s Accept/Reject buttons, independently from parameter changes

Available Enrichment Fields

Field	Description	AI Suggests For
📝 Transcription	Full conversation transcript	Quality assurance, compliance, training agents
📋 Summary	AI-generated call summary	Management reporting, CRM integration, executive dashboards
✅ Success Status	Whether call achieved its goal (based on your Success Criteria)	Performance tracking, conversion optimization
🆔 Call ID	Unique call identifier	System integration, debugging, support tickets
🤖 Agent ID	Playbook/agent identifier	Multi-agent tracking, performance analytics
⏱️ Duration	Call length in seconds	Performance metrics, billing, efficiency analysis
📅 Created	Call start timestamp	Scheduling analysis, trend reporting
📊 Call Status	Final call status (completed, failed, etc.)	Operational monitoring, success rate tracking
✅ Evaluations	Results of evaluation tests	Quality assurance, agent training, compliance
🏷️ Call Type	Inbound/outbound classification	Campaign analysis, channel performance

The Success Status enrichment field only returns meaningful data once you’ve enabled the Success toggle and defined your Success Criteria. Without it, this field will remain empty.

AI Enrichment Recommendations by Use Case

Quality Assurance
Sales Operations
Customer Support

Recommended Fields:

Transcription (compliance review)
Evaluations (quality scoring)
Duration (efficiency tracking)
Agent ID (performance monitoring)

Enable only the enrichment fields you need to keep webhook payloads manageable and reduce processing overhead. The AI helps optimize this selection.

Webhook Configuration

Send call results to your external systems automatically with enhanced security and reliability features.

Setting Up Webhooks

Enable Webhook

Toggle on the webhook option in Output Settings.

Configure Endpoint

Enter your webhook URL and select the HTTP method.

Add Authentication

Configure secure authentication headers (Bearer tokens, API keys).

Test Connection

Use the built-in test feature to verify your webhook receives data correctly.

Enhanced Webhook Features

Feature	Description	Benefit
Retry Logic	Automatic retries for failed webhook calls	Improved reliability
Authentication	Secure header-based authentication	Enhanced security
Payload Filtering	Send only enabled enrichment fields	Reduced bandwidth
Error Logging	Detailed webhook delivery logs	Better debugging

Example Enhanced Webhook Payload

{
  "call_id": "call_abc123",
  "agent_id": "agent_xyz789", 
  "created_at": "2024-01-15T10:30:00Z",
  "duration": 245,
  "status": "completed",
  "success": true,
  "call_type": "outbound",
  "output_params": {
    "customer_interest": "Premium Plan",
    "appointment_scheduled": true,
    "preferred_date": "2024-01-20",
    "satisfaction_score": 5,
    "lead_score": 8,
    "decision_maker": true,
    "pain_points": "Current solution is too expensive",
    "utm_source": "google",
    "utm_campaign": "premium_plan_q1"
  },
  "transcription": "Complete conversation transcript...",
  "summary": "Customer inquired about Premium Plan, qualified as hot lead, appointment scheduled.",
  "evaluations": {
    "greeting_quality": 4.8,
    "needs_assessment": 4.5,
    "closing_effectiveness": 4.2
  }
}

The success field in the payload above reflects the result of the AI’s evaluation against your configured Success Criteria. It will be null or omitted if the Success toggle is disabled.

Best Practices for AI-Enhanced Output Configuration

Write clear, specific descriptions for output parameters and success criteria. The AI uses these descriptions to understand what to extract and how to judge outcomes. Clear descriptions lead to more accurate data extraction and better AI suggestions.

Optimization Guidelines

Leverage AI Suggestions - Start with AI-generated parameters for your agent type
Use Template Library - Quickly add common parameters from the template system
Define Success Criteria Early - Enable Success tracking and describe your desired outcome clearly from the start
Review Pending Changes - Always review AI suggestions in both the Parameters and Enrichment banners before accepting
Be Specific in Descriptions - “Customer’s budget range in USD” vs “budget”
Choose Appropriate Types - Boolean for yes/no, number for quantities, string for text
Optimize Enrichment - Enable only necessary enrichment fields based on AI recommendations
Test in Playground - Verify parameter extraction and success evaluation accuracy before deploying
Secure Webhooks - Use authentication for sensitive data transmission
Monitor Performance - Review extraction accuracy and adjust descriptions as needed

AI Suggestion Acceptance Strategy

Review Agent Context

Ensure the AI correctly understands your agent’s purpose and industry.

Evaluate Suggestions

Review each suggested parameter for relevance to your specific use case.

Customize Descriptions

Modify AI-generated descriptions to match your exact requirements.

Test Integration

Verify that suggested parameters work with your downstream systems.

Smart Output Patterns by Industry

AI-Optimized Sales Call Parameters

{
  "lead_qualification_score": "number",
  "interested_product": "string",
  "budget_range": "string", 
  "decision_timeline": "string",
  "pain_points": "string",
  "competitor_mentioned": "string",
  "next_steps": "string",
  "follow_up_needed": "boolean",
  "decision_maker_present": "boolean",
  "company_size": "string",
  "current_solution": "string",
  "implementation_timeline": "string"
}

AI-Optimized Customer Support Parameters

{
  "issue_category": "string",
  "issue_severity": "string",
  "product_affected": "string",
  "resolution_provided": "boolean",
  "customer_satisfaction": "number",
  "escalation_needed": "boolean", 
  "resolution_time": "number",
  "follow_up_required": "boolean",
  "root_cause": "string",
  "solution_type": "string",
  "knowledge_base_used": "string"
}

AI-Optimized Healthcare Scheduling Parameters

{
  "appointment_date": "string",
  "appointment_time": "string",
  "service_type": "string",
  "provider_requested": "string",
  "insurance_verified": "boolean",
  "symptoms_mentioned": "string",
  "urgency_level": "string",
  "confirmation_method": "string",
  "reminder_preference": "string",
  "special_accommodations": "string"
}

Working with Copilot Suggestions

Understanding Suggestion Context

When the AI Copilot suggests output parameters, you’ll see:

Parameter Name - Optimized field name following naming conventions
Data Type - Recommended type (string, number, boolean) based on expected values
Description - Clear, specific extraction instruction
Reasoning - Why this parameter is relevant to your agent and industry
Usage Examples - Sample values to expect

Suggestion Management Workflow

Batch Operations
Individual Review
Advanced Management

Accept All - Apply all suggestions in the relevant banner (Parameters or Enrichment) simultaneously
Reject All - Dismiss all pending changes in that banner
Preview Impact - See how changes affect your configuration

Customizing AI Suggestions

You can modify AI suggestions before accepting them. This allows you to fine-tune parameter names, adjust descriptions for your specific business context, or change data types based on your integration requirements.

Advanced Integration Scenarios

Multi-Agent Output Coordination

For organizations running multiple specialized agents:

Consistent Naming - Use AI suggestions to maintain parameter naming consistency
Cross-Agent Fields - Implement common fields across different agent types
Enrichment Alignment - Standardize enrichment settings for unified reporting
Unified Success Criteria - Define consistent success language across similar agent types for comparable reporting

CRM Integration Optimization

Map CRM Fields

Match AI-suggested parameters to your CRM field structure.

Data Type Alignment

Ensure parameter types match your CRM field requirements.

Webhook Testing

Test webhook integration with sample AI-generated data.

Error Handling

Implement proper error handling for parameter mapping issues.

Next Steps

Greetings

Configure welcome messages with AI assistance

Evaluations

Set up quality tests and scoring

​What are Output Settings?

​Success Evaluation

​The Success Toggle

​Writing Effective Success Criteria

​AI Copilot Smart Suggestions

​How AI Suggestions Work

​Example AI Suggestions by Agent Type

​Copilot Change Management

​Pending Changes Banners

​Managing Pending Changes

​Change Operation Labels

​Output Parameters with Templates

​Parameter Templates System

​Available Parameter Templates

​Creating Custom Parameters

​Parameter Properties

​Advanced Text Mode with Syntax Highlighting

​Text Mode Features

​Using Text Mode

​Example JSON Configuration

​AI-Enhanced Output Enrichment

​Smart Enrichment Suggestions

​Enrichment Change Management

​Available Enrichment Fields

​AI Enrichment Recommendations by Use Case

​Webhook Configuration

​Setting Up Webhooks

​Enhanced Webhook Features

​Example Enhanced Webhook Payload

​Best Practices for AI-Enhanced Output Configuration

​Optimization Guidelines

​AI Suggestion Acceptance Strategy

​Smart Output Patterns by Industry

​AI-Optimized Sales Call Parameters

​AI-Optimized Customer Support Parameters

​AI-Optimized Healthcare Scheduling Parameters

​Working with Copilot Suggestions

​Understanding Suggestion Context

​Suggestion Management Workflow

​Customizing AI Suggestions

​Advanced Integration Scenarios

​Multi-Agent Output Coordination

​CRM Integration Optimization

​Next Steps

Greetings

Evaluations

What are Output Settings?

Success Evaluation

The Success Toggle

Writing Effective Success Criteria

AI Copilot Smart Suggestions

How AI Suggestions Work

Example AI Suggestions by Agent Type

Copilot Change Management

Pending Changes Banners

Managing Pending Changes

Change Operation Labels

Output Parameters with Templates

Parameter Templates System

Available Parameter Templates

Creating Custom Parameters

Parameter Properties

Advanced Text Mode with Syntax Highlighting

Text Mode Features

Using Text Mode

Example JSON Configuration

AI-Enhanced Output Enrichment

Smart Enrichment Suggestions

Enrichment Change Management

Available Enrichment Fields

AI Enrichment Recommendations by Use Case

Webhook Configuration

Setting Up Webhooks

Enhanced Webhook Features

Example Enhanced Webhook Payload

Best Practices for AI-Enhanced Output Configuration

Optimization Guidelines

AI Suggestion Acceptance Strategy

Smart Output Patterns by Industry

AI-Optimized Sales Call Parameters

AI-Optimized Customer Support Parameters

AI-Optimized Healthcare Scheduling Parameters

Working with Copilot Suggestions

Understanding Suggestion Context

Suggestion Management Workflow

Customizing AI Suggestions

Advanced Integration Scenarios

Multi-Agent Output Coordination

CRM Integration Optimization

Next Steps