Copilot - Meetzy Docs

What is Copilot?

Copilot is an advanced AI-powered system engineer integrated into the Advanced Editor. It goes far beyond simple prompt editing - Copilot can configure your entire agent system including input parameters, output settings, evaluations, audio expressiveness, and core configurations. Using sophisticated structured editing and intelligent analysis, Copilot transforms your agent based on natural language instructions and real call data.

Copilot has evolved from a prompt assistant to a complete system engineer - like having Data from Star Trek, but specifically designed for agent configuration. It can modify prompts, configure parameters, set up evaluations, update basic settings, adjust audio expressiveness tags, create and update tests from call history, and even suggest improvements - all through natural conversation and targeted operations. Beyond plain text, Copilot can also reply with rich visual blocks - stat panels, tables, charts, and cards - to make diagnostics and analysis instantly scannable.

Accessing Copilot

Copilot is located in the right panel of the Advanced Editor. Click the “Copilot” tab or press ⌘J to access it quickly.

Meet the Copilot Orb

Copilot now has a face: a gradient orb avatar that represents Meetzy across the entire chat experience, replacing the generic bot icon.

Idle State
Active State

A slow, ambient breathing animation while Copilot is waiting for your next message - a calm, always-present visual anchor in the empty state and next to every assistant reply.

The orb appears in three places: the welcome screen hero, the avatar next to each assistant message, and the typing/thinking indicator - giving Copilot a consistent, recognizable identity throughout the conversation. Its state (idle or active) always reflects whether Copilot is waiting for you or actively working, so you always know at a glance whether it’s inactive or engaged.

Enhanced Welcome Experience

When you first open Copilot, you’ll see a clean welcome screen led by the Copilot orb, followed by four quick action cards to get started immediately without typing a single word:

Quick Action Cards
One-Click Setup

Four color-coded cards for the most common engineering workflows:

🟣 Diagnose performance — analyzes real metrics, failed evaluations, and relevant calls, then shows the key findings visually (KPIs & call evidence)
🌹 Find recurring failures — scans failed evaluations across your call history and returns a table of recurring issues with real examples
🟢 Create a regression test — turns a real failed call into an automated regression test so the same bug never ships again
🟡 Improve and verify — updates the prompt based on the current diagnosis and suggests how to verify the change afterward

Instant analysis and fixes with a single click:

Click "Diagnose performance" → Runs a full performance diagnosis with visual KPIs
Click "Find recurring failures" → Surfaces failure patterns in a table with call evidence
Click "Create a regression test" → Builds a regression test from a real failed call
Click "Improve and verify" → Improves the prompt and proposes a verification plan

Each card pre-fills an optimized prompt and starts the conversation immediately - no typing required to get your first insight.

These quick actions are designed to close the loop: diagnose → find failures → fix with a regression test → improve and verify. Together they form a fast, guided workflow for continuously improving your agent.

Response Output Types

Copilot’s replies are no longer limited to plain text. Depending on what you ask, Copilot decides the most effective format for its answer and can mix multiple output types in a single response:

Plain Text
Rich Content Blocks

Conversational explanations streamed in real time - used for reasoning, recommendations, step-by-step instructions, and any answer that reads best as natural language.

Rich Response Blocks

Copilot doesn’t just reply with text anymore - when the situation calls for it, it renders rich, interactive blocks directly inside the chat, making diagnostics and analysis far easier to scan than a wall of text. These blocks stream in the same way as regular messages, so you see them appear live as Copilot builds its answer.

Stat Grids
Tables
Charts
Cards

At-a-glance KPI cards for quick diagnostics:

Compact grid of metric cards (e.g. success rate, average duration, failed evaluations)
Optional delta and trend indicators (up, down, flat) to show change over time
Color-coded tones (neutral, info, success, warning, danger) to highlight what needs attention
Optional hint text under each stat for extra context

Perfect for summarizing “Diagnose performance” results in a single scannable view.

Rich blocks stream in alongside regular text, so Copilot can explain a finding in natural language and immediately back it up with a stat grid, table, chart, or card - all within the same response. Look for these blocks whenever you ask Copilot to diagnose, compare, or summarize data - they turn a long paragraph into something you can scan in seconds.

System Engineering Capabilities

Copilot now operates as a comprehensive system engineer that can modify every aspect of your agent configuration:

Prompt Engineering
Parameter Configuration
Evaluation & Testing
Basic Configuration
Audio Expressiveness

Advanced Structured Editing: Precise modifications using the EditPlan system

replace_section: Completely replaces targeted sections with improved content
insert_after: Adds new content after specified sections or markers
append_section: Adds entirely new sections to the end of your prompt
delete_section: Removes outdated or redundant sections cleanly

Uses intelligent boundary detection to preserve document structure while making surgical improvements.

Direct Control Over Voice Emotion Tags (Eleven v3)

Adds, modifies, or removes expressive audio tags (e.g. [excited], [whispers], [curious]) directly in the prompt
Suggests contextually appropriate tags based on the agent’s role and tone
Keeps tag usage consistent across the prompt to avoid overacting or flat delivery
Applies changes through the same purple-highlight review flow as other edits

Enhanced SSE Protocol

Copilot uses a streamlined event system for real-time feedback across all system modifications:

Event Type	Description	Visual Indicator	Applies To
thinking	AI analyzing your request	⏱️ Timer with seconds counter	All operations
text	Streaming explanation content	📝 Real-time text appearing	All operations
edit	Structured prompt modification	✏️ Diff preview with operation details	Prompt changes
input_change	Input parameter modification	🔧 Parameter change card	Input parameters
output_change	Output setting update	📤 Output configuration card	Output settings
enrichment_set	Output enrichment update	⚡ Enrichment content preview	Output enrichment
evaluation_change	Evaluation criteria update	📊 Evaluation configuration	Evaluations
test_create	Automated test generation	🧪 Test scenario preview	Test creation
test_update	Existing test modification	🔁 Test diff preview	Test updates
agent_config	Basic settings update	⚙️ Configuration preview	Core settings
audio_tags_set	Expressive audio tag update	🎙️ Tag list preview	Audio expressiveness
rich_block	Rich UI block (stat grid, table, chart, card)	📊 Inline visual component	Diagnostics & analysis
complete	Operation finished	✅ Summary with next steps	All operations
error	Operation failed	❌ Error with suggested fixes	All operations

The rich_block event carries a blockType (stat_grid, table, chart, or card) plus a structured data payload. The frontend renders it as a native component inline with the rest of the streamed conversation, right next to any text events in the same response.

Visual Change Management System

Purple Highlight Protocol

When Copilot generates configuration changes, distinctive visual indicators guide your review:

Pending Changes Banner
Change Cards
Cross-Panel Integration

Purple-highlighted banners appear at the top of relevant panels:

Copilot sparkle icon with “Copilot Suggestions” title
Change summary: “3 new parameters, 1 evaluation”
Bulk controls: Accept All/Reject All buttons
Panel navigation: Direct links to affected configuration areas

Purple notification dots appear on affected panel tabs to ensure you don’t miss suggestions.

Change Type Indicators

Change Type	Badge Color	Operation	Description
Add	🟢 Green	ADD	New parameters, evaluations, or settings
Modify	🟡 Yellow	MODIFY	Changes to existing configurations
Remove	🔴 Red	REMOVE	Deletion of outdated or redundant items

Comprehensive System Configuration

Copilot intelligently suggests changes across all agent configuration areas:

Input Parameter Operations
Output & Enrichment Configuration
Evaluation Criteria Setup
Basic Settings & Configuration
Audio Expressiveness Tags

Smart Parameter Suggestions based on comprehensive analysis:

Contextual Generation

Copilot analyzes your agent’s role and referenced calls to suggest relevant input parameters:

@call:abc123 The customer mentioned their budget repeatedly 
but we didn't capture it. Add budget tracking parameters.

Results in contextually relevant budget range and validation parameters.

Change Operations

Three types of parameter changes:

🟢 Add: New parameters with proper validation and descriptions
🟡 Modify: Updates to existing parameter configurations
🔴 Remove: Clean up outdated or redundant parameters

Visual Review System

Purple highlighting in Input Parameters panel shows pending changes with individual accept/reject controls.

Intelligent Output Field & Processing Setup:

The agent collected the customer's name and interest level 
but we're missing their preferred contact method. Add comprehensive 
output fields and enrichment instructions.

Copilot suggests:

Contextually relevant output parameters
Data enrichment instructions for processing captured information
Webhook configuration recommendations
Output validation and formatting rules

Performance Measurement Configuration based on agent goals:

Create evaluations to measure conversation quality, goal achievement, 
and customer satisfaction for this appointment scheduling agent.

Automatically generates:

Conversation Quality: Clarity, professionalism, engagement scoring
Goal Achievement: Appointment booking success tracking
Customer Satisfaction: Experience evaluation criteria
Process Adherence: Compliance with scheduling procedures

Core Agent Setup from natural language:

Update the agent name to "Sarah - Appointment Scheduler" and 
make the greetings more friendly while keeping them professional.

Copilot can modify:

Agent name and identity
Inbound/outbound greeting prompts
Phone number assignment
Voice and personality settings

Direct Tag Editing for more natural, emotionally-aware voice delivery:

Add expressive tags so the agent sounds more empathetic when 
handling complaints, and more upbeat during the closing.

Copilot can:

Insert new expressiveness tags at the right points in the prompt
Modify existing tags that feel overused, misplaced, or mismatched with tone
Remove tags that conflict with the agent’s intended personality
Keep tag density balanced so delivery doesn’t sound exaggerated

Advanced Test Creation & Updates from Call History

One of Copilot’s most powerful features is generating - and maintaining - comprehensive test suites based on real call data:

Test Generation from Calls
Updating Existing Tests
Test Types Generated
Intelligent Test Structure

Transform Call History into Automated Tests:

@call:abc123 This call shows great objection handling. 
Create a test to ensure the agent maintains this quality.

Copilot automatically extracts:

Chat History: Complete conversation flow from the call
Success Conditions: What made this call successful
Success Examples: Specific agent responses that worked well
Failure Examples: Alternative responses to avoid
Dynamic Variables: Contextual information that can be varied in tests

Refine tests in place instead of duplicating them:

Update the "Objection handling - pricing" test to include the 
new discount policy and tighten the success condition.

Copilot can modify an existing test’s:

Name and description
Chat history to reflect updated conversation flows
Success condition as your requirements evolve
Success/failure examples to match current expectations
Dynamic variables to cover new scenarios

Test updates apply to the specific test referenced, so your test suite stays clean and versioned instead of accumulating near-duplicate tests over time.

Voice Input & Call Context Analysis

Enhanced natural interaction capabilities for comprehensive system configuration:

Voice Command System
Call Context Integration

Activation Methods

Two ways to start voice recording:

Hold microphone button in the input field
Double-press Shift to begin hands-free recording

Natural Speech Processing

Speak conversationally about any system changes:

“Add input parameters for customer budget and timeline preferences”
“Create evaluations to measure appointment booking success”
“Update the greetings to be more friendly and professional”
“Based on call A-B-C-1-2-3, improve the pricing objection handling”

System-Wide Understanding

Copilot interprets requests across all configuration areas:

Voice converts to text automatically
AI determines which systems need updates
Generates appropriate changes across multiple panels

Powerful call reference system for data-driven improvements:

Using Call References

@call:abc123 The agent failed to capture the customer's budget 
and preferred contact method. Add the missing parameters and 
improve the data collection prompts.

When you reference a call, Copilot:

Automatically loads the full transcript
Analyzes conversation flow and data gaps
Suggests missing input/output parameters
Updates prompts based on specific context
Creates relevant evaluation criteria
Generates or updates tests based on call patterns

Comprehensive Call Analysis

Referenced calls provide rich context for:

Missing Data Identification: What information wasn’t captured
Process Gap Analysis: Where the conversation broke down
Parameter Suggestions: Input/output fields based on actual needs
Evaluation Criteria: Performance metrics based on real outcomes
Test Creation & Updates: New or refined automated test scenarios from actual interactions

Accepting and Rejecting System Changes

Granular Change Control Across All Panels

Cross-Panel Review

Check all affected configuration areas:

Look for purple notification dots on panel tabs
Review change cards in each affected area
Understand the impact of each suggested modification

Selective Application

Accept only relevant changes across the system:

Individual accept/reject buttons for each suggestion
Bulk accept/reject options within each panel
Changes apply immediately to respective configurations

Live System Updates

See comprehensive results instantly:

Input Parameters panel updates with new fields
Output Settings reflect new capture requirements
Evaluations panel shows new measurement criteria
Basic Settings display updated configurations
Tests panel includes newly generated or updated test scenarios
Prompt reflects updated audio expressiveness tags

Comprehensive Change Management

System-wide operations for managing multiple configuration updates:

Accept All in Panel - Apply all suggestions within a specific configuration area
Accept All Changes - Apply all pending suggestions across the entire system
Reject All in Panel - Dismiss suggestions within a specific area
Reject All Changes - Dismiss all pending suggestions system-wide

System-wide bulk actions affect multiple configuration areas simultaneously. Review individual suggestions across all panels before using “Accept All Changes”.

Advanced System Engineering Examples

Comprehensive Agent Setup from Call Analysis

@call:xyz789 The agent struggled with budget questions, 
didn't capture contact preferences, and we can't measure success. 
Set up a complete configuration for this sales agent.

Copilot generates:

Prompt updates: Better budget objection handling instructions
Input parameters: Budget range, contact method preferences, lead source
Output parameters: Qualification score, next steps, contact preferences
Evaluations: Lead quality, objection handling, conversion tracking
Basic settings: Professional but approachable greeting updates
Tests: Automated scenarios based on the analyzed call patterns

Multi-System Integration Configuration

Add comprehensive lead qualification for this real estate agent. 
Include budget tracking, timeline parameters, property preferences, 
qualification scoring, and success evaluations.

Results in coordinated changes across:

Input Parameters: Budget, timeline, property type, location preferences
Output Settings: Qualification score, lead temperature, next actions
Prompt Instructions: Qualification process and scoring guidelines
Evaluations: Lead quality metrics, conversion tracking, customer satisfaction
Output Enrichment: Lead processing and CRM integration instructions
Tests: Complete qualification workflow scenarios

Performance Optimization from Multiple Calls

@call:call1 @call:call2 @call:call3 These calls show the agent 
needs better appointment scheduling process, missing confirmation 
parameters, and we can't track booking success rates.

Comprehensive system updates:

Analyzes patterns across multiple call transcripts
Updates prompts for consistent scheduling process
Adds confirmation and calendar integration parameters
Creates appointment booking success evaluations
Suggests test scenarios based on real conversation patterns
Generates regression tests for quality assurance

Test Creation from Successful Calls

@call:perfect123 This was a perfect call where the agent handled 
all objections and booked the appointment. Create comprehensive 
tests to maintain this quality.

Intelligent test generation:

Extracts complete conversation flow
Identifies successful objection handling patterns
Creates multiple test scenarios with variations
Sets up quality benchmarks based on the successful call
Includes dynamic variables for different customer personalities

Updating a Test After a Policy Change

Update the "Refund policy objection" test - the refund window changed 
from 30 to 60 days, update the success condition and chat history accordingly.

Copilot performs:

Locates the existing test by name
Updates the success condition to reflect the new 60-day window
Adjusts chat history examples that reference the old policy
Leaves unrelated tests untouched

Adjusting Audio Expressiveness for a More Natural Voice

The agent sounds too flat during the apology section. Add expressive 
tags there and remove the excessive excitement tags in the greeting.

Copilot performs:

Inserts empathetic expressiveness tags (e.g. [softly], [apologetic]) around the apology section
Removes or tones down over-the-top tags in the greeting
Shows the change through the purple-highlight diff review before applying

Diagnosing Performance with Rich Blocks

Click "Diagnose performance" on the welcome screen

Copilot responds with:

A short natural-language summary of the agent’s current state
A stat grid showing success rate, average call duration, and failed evaluations with trend indicators
A table listing the most relevant calls tied to those metrics
A closing card with tone-coded recommendations on what to fix first

Finding Recurring Failures

Click "Find recurring failures" on the welcome screen

Copilot responds with:

A table of recurring failure patterns extracted from failed evaluations
Each row includes the failure type, frequency, and a linked call example
A follow-up suggestion to turn the top failure into a regression test

Visualizing Trends with a Chart

Show me how our success rate has changed over the last 4 weeks.

Copilot responds with:

A short summary of the overall trend direction
A line chart plotting success rate by week
A closing note on whether the trend is improving, flat, or declining

Quick Start with Predefined Actions

The welcome interface provides four instant setup options:

Diagnose Performance
Find Recurring Failures
Create a Regression Test
Improve and Verify

One-click performance diagnostics:

Pulls real metrics, failed evaluations, and relevant calls
Renders findings as stat grids, tables, and charts
Closes with a card summarizing what needs attention

Perfect for: Getting a fast health check on any agent

Next Step Suggestions & System Recommendations

After completing comprehensive changes, Copilot provides intelligent system-wide recommendations:

Related System Improvements
Configuration Completeness
Quality Assurance Prompts

Conversation History & Iterative Configuration

Build sophisticated system improvements through multi-turn conversations:

User: Set up lead qualification for this sales agent

Copilot: [Creates input params, output fields, evaluations, and prompt updates]

User: Good, but make the budget qualification less aggressive

Copilot: [Refines parameters and prompt approach across affected areas]

User: Add follow-up scheduling and CRM integration

Copilot: [Adds scheduling params, webhook configs, and integration tests]

User: @call:abc123 Create tests based on this successful call

Copilot: [Generates comprehensive test scenarios from call history]

User: Update the "Follow-up scheduling" test - we now offer same-day slots

Copilot: [Updates the existing test's chat history and success condition in place]

Copilot maintains conversation context across multiple system modification requests, allowing for iterative refinement of your entire agent configuration through natural dialogue.

Comprehensive System Tasks

Complete Agent Setup from Scratch

Create a professional appointment scheduling agent named "Sarah" 
with comprehensive lead qualification, appointment booking parameters, 
success tracking evaluations, friendly but professional greetings,
and test scenarios for quality assurance.

Data-Driven System Optimization

@call:abc123 @call:def456 Analyze these problematic calls and 
optimize the entire agent system - fix the prompts, add missing 
parameters, create better evaluations, set up proper testing,
and generate quality assurance tests.

Multi-System Integration Configuration

Set up this agent for CRM integration with Salesforce - add all 
necessary input and output parameters, webhook configurations, 
data enrichment instructions, integration success evaluations,
and comprehensive test scenarios for the integration workflow.

Performance-Driven System Updates

Our conversion rate is low. Analyze the current setup and improve 
the qualification process, add better objection handling, create 
conversion tracking evaluations, optimize output data capture,
and generate tests to maintain improved performance.

Test Suite Creation and Maintenance from Call History

@call:perfect1 @call:perfect2 @call:perfect3 These are our best calls.
Create a comprehensive test suite based on these successful patterns,
and update any existing tests that no longer match current policies.

Compliance and Quality Assurance Setup

Add comprehensive compliance checking for this financial services 
agent - include required disclosures in prompts, compliance tracking 
parameters, regulatory evaluation criteria, audit trail outputs,
and compliance verification tests.

Voice Expressiveness Tuning

Review the current audio tags for this customer support agent and 
adjust them so empathy comes through during complaints without 
sounding exaggerated anywhere else.

Shortcut	Action
⌘J	Open/focus Copilot panel
Shift + Shift	Start/stop voice recording
⌘Enter	Send message to Copilot
Escape	Close suggestions or cancel recording
⌘1	Navigate to Basic Settings panel
⌘2	Navigate to Greetings panel
⌘3	Navigate to Input Parameters panel
⌘6	Navigate to Evaluations panel
⌘7	Navigate to Output Settings panel
⌘8	Navigate to Tests panel
⌘G	Navigate to Git Changes panel

Best Practices for System Engineering

Start with Quick Actions

Use the welcome-screen cards for common workflows:

Start with “Diagnose performance” for a comprehensive health check
Use “Find recurring failures” to root-cause quality issues
Click “Create a regression test” to lock in a fix
Try “Improve and verify” to close the loop on a diagnosis

Use Comprehensive Natural Language

Describe complete system needs rather than isolated changes:Good: “Set up complete lead qualification with budget tracking, contact preferences, scoring system, success evaluations, and quality assurance tests”Limited: “Add a budget parameter”Comprehensive requests generate coordinated improvements across the entire system.

Leverage Real Call Data for System Design

Reference actual calls to drive system-wide improvements:

Use @call:ID syntax to provide specific examples of system gaps
Let Copilot analyze real conversation data across all configuration areas
Address actual problems with coordinated parameter, prompt, and evaluation updates
Generate tests from successful calls, and update existing ones as requirements change

Read the Rich Blocks Before Acting

Use stat grids, tables, and charts to validate assumptions:

Check trend indicators in stat grids before deciding what to prioritize
Scan failure tables for patterns rather than reacting to a single call
Use the closing card’s recommendations as your next prompt to Copilot
Ask Copilot directly for a chart or table if a text answer feels hard to scan

Review All Affected Configuration Areas

Check every panel with purple notification indicators:

Input Parameters for new data collection fields
Output Settings for enhanced data capture
Evaluations for performance measurement
Basic Settings for core configuration updates
Tests panel for newly generated or updated test scenarios
Prompt for updated audio expressiveness tags
Consider the impact across the entire agent system

Think in System Integration Terms

Consider how changes work together across the platform:

New input parameters should have matching output fields
Prompt changes need corresponding evaluation criteria
Output configurations should include proper enrichment instructions
Successful configurations should include test scenarios for quality assurance
Test comprehensive workflows after system-wide changes

Use Voice for Complex System Requests

Natural speech excels for describing comprehensive system needs:

“Set up complete sales qualification with lead scoring, CRM integration, and quality tests”
“Based on these three problem calls, optimize the entire agent system with comprehensive testing”
Let conversational input guide holistic system improvements

Keep Tests Current Instead of Duplicating Them

Update existing tests as your agent evolves:

Reference an existing test by name to update its conditions instead of creating a near-duplicate
Use test updates after prompt or policy changes to keep your suite accurate
Reserve new test creation for genuinely new scenarios or call patterns

Fine-Tune Audio Expressiveness Deliberately

Treat audio tags as part of the prompt, not an afterthought:

Ask Copilot to review tag usage periodically, not just when something sounds wrong
Balance expressiveness so it enhances key moments without becoming distracting
Verify changes by listening to a Playground call after tags are updated

Follow Cross-Panel Notification Cues

Purple indicators guide you to all affected system areas:

Check every panel with notification dots
Don’t miss configuration changes in different areas
Review generated or updated tests in the Tests panel
Use seamless navigation to review comprehensively

Test System Changes Comprehensively

Validate complete system modifications before deploying:

Test new parameters with real data in Playground
Verify evaluation criteria work with actual conversations
Validate output configurations and webhook integrations
Run generated or updated tests to ensure quality maintenance
Run regression tests across the entire conversation flow

Iterate on Complete System Understanding

Build comprehensive agent configurations through dialogue:

Start with high-level system requirements
Refine specific areas through follow-up conversations
Test and adjust system-wide configurations
Generate and refine tests based on real performance
Build understanding of how all components work together

Next Steps

Input Parameters

Configure comprehensive input parameters with AI suggestions from Copilot

Output Settings

Set up output fields, enrichment, and webhooks with intelligent recommendations

Evaluations

Create comprehensive performance measurements with Copilot’s system analysis

Basic Settings

Configure core agent settings with AI-powered optimization

Tests

Create and maintain comprehensive automated tests for your entire agent system, including scenarios generated and updated from call history

Playground

Test your complete system configuration with real data and validate generated test scenarios

​What is Copilot?

​Accessing Copilot

​Meet the Copilot Orb

​Enhanced Welcome Experience

​Response Output Types

​Rich Response Blocks

​System Engineering Capabilities

​Enhanced SSE Protocol

​Visual Change Management System

​Purple Highlight Protocol

​Change Type Indicators

​Comprehensive System Configuration

​Advanced Test Creation & Updates from Call History

​Voice Input & Call Context Analysis

​Using Call References

​Comprehensive Call Analysis

​Accepting and Rejecting System Changes

​Granular Change Control Across All Panels

​Comprehensive Change Management

​Advanced System Engineering Examples

​Comprehensive Agent Setup from Call Analysis

​Multi-System Integration Configuration

​Performance Optimization from Multiple Calls

​Test Creation from Successful Calls

​Updating a Test After a Policy Change

​Adjusting Audio Expressiveness for a More Natural Voice

​Diagnosing Performance with Rich Blocks

​Finding Recurring Failures

​Visualizing Trends with a Chart

​Quick Start with Predefined Actions

​Next Step Suggestions & System Recommendations

​Conversation History & Iterative Configuration

​Comprehensive System Tasks

​Complete Agent Setup from Scratch

​Data-Driven System Optimization

​Multi-System Integration Configuration

​Performance-Driven System Updates

​Test Suite Creation and Maintenance from Call History

​Compliance and Quality Assurance Setup

​Voice Expressiveness Tuning

​Keyboard Shortcuts for System Navigation

​Best Practices for System Engineering

​Next Steps

Input Parameters

Output Settings

Evaluations

Basic Settings

Tests

Playground

What is Copilot?

Accessing Copilot

Meet the Copilot Orb

Enhanced Welcome Experience

Response Output Types

Rich Response Blocks

System Engineering Capabilities

Enhanced SSE Protocol

Visual Change Management System

Purple Highlight Protocol

Change Type Indicators

Comprehensive System Configuration

Advanced Test Creation & Updates from Call History

Voice Input & Call Context Analysis

Using Call References

Comprehensive Call Analysis

Accepting and Rejecting System Changes

Granular Change Control Across All Panels

Comprehensive Change Management

Advanced System Engineering Examples

Comprehensive Agent Setup from Call Analysis

Multi-System Integration Configuration

Performance Optimization from Multiple Calls

Test Creation from Successful Calls

Updating a Test After a Policy Change

Adjusting Audio Expressiveness for a More Natural Voice

Diagnosing Performance with Rich Blocks

Finding Recurring Failures

Visualizing Trends with a Chart

Quick Start with Predefined Actions

Next Step Suggestions & System Recommendations

Conversation History & Iterative Configuration

Comprehensive System Tasks

Complete Agent Setup from Scratch

Data-Driven System Optimization

Multi-System Integration Configuration

Performance-Driven System Updates

Test Suite Creation and Maintenance from Call History

Compliance and Quality Assurance Setup

Voice Expressiveness Tuning

Keyboard Shortcuts for System Navigation

Best Practices for System Engineering

Next Steps