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Brooke Harris
Brooke Harris

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I Didn’t Know Copilot Had Two Modes — Ask for Devs, Agent for Non-Devs

#githubcopilot #agentaichallenge #development #nocode

Microsoft Copilot represents a paradigm shift in how different user groups interact with AI assistance. Understanding the distinction between "ask mode" for developers and "agent mode" for non-developers reveals how AI can be optimized for different workflows, skill levels, and use cases.

Understanding the Two Modes

Ask Mode (Developer-Focused)

Ask mode is designed for users who understand the underlying systems, can interpret technical responses, and prefer direct control over the AI interaction. This mode assumes technical literacy and provides more granular, detailed responses.

Agent Mode (Non-Developer-Focused)

Agent mode functions as an autonomous assistant that can take actions on behalf of the user, handling complex multi-step processes with minimal technical input required from the user.

Developer Use Cases: Ask Mode

1. Code Generation and Assistance

Direct Code Queries: 

// Developer ask mode interaction
Developer: "Generate a React component for user authentication with form validation"

Copilot Response:
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jsx
import React, { useState } from 'react';

const AuthForm = () => {
const [formData, setFormData] = useState({
email: '',
password: ''
});
const [errors, setErrors] = useState({});

const validateForm = () => {
const newErrors = {};

if (!formData.email) {
  newErrors.email = 'Email is required';
} else if (!/\S+@\S+\.\S+/.test(formData.email)) {
  newErrors.email = 'Email is invalid';
}

if (!formData.password) {
  newErrors.password = 'Password is required';
} else if (formData.password.length < 8) {
  newErrors.password = 'Password must be at least 8 characters';
}

setErrors(newErrors);
return Object.keys(newErrors).length === 0;
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};

const handleSubmit = (e) => {
e.preventDefault();
if (validateForm()) {
// Handle authentication
console.log('Form submitted:', formData);
}
};

return (



type="email"
placeholder="Email"
value={formData.email}
onChange={(e) => setFormData({...formData, email: e.target.value})}
/>
{errors.email && {errors.email}}


type="password"
placeholder="Password"
value={formData.password}
onChange={(e) => setFormData({...formData, password: e.target.value})}
/>
{errors.password && {errors.password}}

Login

);
};

export default AuthForm;

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Technical Problem Solving: 

# Developer query example
Developer: "How do I optimize this database query for better performance?"

# Original query
SELECT u.*, p.*, o.* 
FROM users u 
JOIN profiles p ON u.id = p.user_id 
JOIN orders o ON u.id = o.user_id 
WHERE u.created_at > '2024-01-01'

# Copilot optimization suggestions:
"""
1. Add indexes:
   CREATE INDEX idx_users_created_at ON users(created_at);
   CREATE INDEX idx_profiles_user_id ON profiles(user_id);
   CREATE INDEX idx_orders_user_id ON orders(user_id);

2. Select only needed columns:
   SELECT u.id, u.name, p.bio, o.total 
   FROM users u...

3. Consider pagination:
   LIMIT 100 OFFSET 0

4. Use EXISTS for conditional joins:
   SELECT u.* FROM users u 
   WHERE u.created_at > '2024-01-01'
   AND EXISTS (SELECT 1 FROM profiles p WHERE p.user_id = u.id)
"""
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2. Architecture and Design Decisions

System Design Consultation: 

# Developer architectural query
Developer: "Design a microservices architecture for an e-commerce platform"

Copilot Response:
# E-commerce Microservices Architecture

services:
  user-service:
    responsibilities:
      - User authentication
      - Profile management
      - User preferences
    database: PostgreSQL
    api: REST/GraphQL

  product-service:
    responsibilities:
      - Product catalog
      - Inventory management
      - Product search
    database: MongoDB
    search: Elasticsearch

  order-service:
    responsibilities:
      - Order processing
      - Order history
      - Order status tracking
    database: PostgreSQL
    messaging: RabbitMQ

  payment-service:
    responsibilities:
      - Payment processing
      - Payment methods
      - Transaction history
    database: PostgreSQL (encrypted)
    external: Stripe, PayPal APIs

  notification-service:
    responsibilities:
      - Email notifications
      - SMS alerts
      - Push notifications
    messaging: Apache Kafka

infrastructure:
  api-gateway: Kong/AWS API Gateway
  service-mesh: Istio
  monitoring: Prometheus + Grafana
  logging: ELK Stack
  container: Docker + Kubernetes
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3. Debugging and Troubleshooting

Error Analysis: 

// Developer debugging query
Developer: "Why is this async function not working properly?"

async function fetchUserData(userId) {
  try {
    const response = fetch(`/api/users/${userId}`);
    const userData = response.json();
    return userData;
  } catch (error) {
    console.error('Error:', error);
  }
}

// Copilot identifies issues:
/*
Issues found:
1. Missing 'await' keywords
2. Not checking response.ok
3. Error handling could be improved

Corrected version:
*/
async function fetchUserData(userId) {
  try {
    const response = await fetch(`/api/users/${userId}`);

    if (!response.ok) {
      throw new Error(`HTTP error! status: ${response.status}`);
    }

    const userData = await response.json();
    return userData;
  } catch (error) {
    console.error('Error fetching user data:', error);
    throw error; // Re-throw to let caller handle
  }
}
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4. Code Review and Optimization

Performance Analysis: 

# Developer code review query
Developer: "Review this Python function for performance improvements"

def process_large_dataset(data):
    results = []
    for item in data:
        if item['status'] == 'active':
            processed = expensive_operation(item)
            if processed['score'] > 0.8:
                results.append(processed)
    return results

# Copilot suggestions:
"""
Performance improvements:

1. Use list comprehension with filtering:
   results = [
       processed for item in data 
       if item['status'] == 'active'
       for processed in [expensive_operation(item)]
       if processed['score'] > 0.8
   ]

2. Use filter() and map() for functional approach:
   active_items = filter(lambda x: x['status'] == 'active', data)
   processed_items = map(expensive_operation, active_items)
   results = [item for item in processed_items if item['score'] > 0.8]

3. For very large datasets, consider using generators:
   def process_large_dataset_generator(data):
       for item in data:
           if item['status'] == 'active':
               processed = expensive_operation(item)
               if processed['score'] > 0.8:
                   yield processed

4. Parallel processing for CPU-intensive operations:
   from multiprocessing import Pool

   def process_item(item):
       if item['status'] == 'active':
           processed = expensive_operation(item)
           if processed['score'] > 0.8:
               return processed
       return None

   with Pool() as pool:
       results = [r for r in pool.map(process_item, data) if r is not None]
"""
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Non-Developer Use Cases: Agent Mode

1. Business Process Automation

Automated Report Generation: 

Non-Developer: "Create a monthly sales report and send it to the management team"

Agent Mode Process:
1. 🔍 Connects to sales database
2. 📊 Extracts sales data for the current month
3. 📈 Generates charts and visualizations
4. 📝 Creates formatted report document
5. 📧 Composes email with report attached
6. 👥 Sends to management distribution list
7. 📅 Schedules next month's report automatically

User sees: "Monthly sales report has been generated and sent to management. Next report scheduled for [date]."
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Customer Service Automation: 

Non-Developer: "Handle customer inquiries about order status"

Agent Mode Process:
1. 📨 Monitors customer service inbox
2. 🔍 Identifies order status inquiries
3. 🗃️ Looks up order information in system
4. 📝 Generates personalized response
5. 📧 Sends response to customer
6. 📊 Updates customer service metrics
7. 🚨 Escalates complex issues to human agents

User sees: "Handling 15 order status inquiries. 12 resolved automatically, 3 escalated to human agents."
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2. Content Creation and Management

Social Media Management: 

Non-Developer: "Create and schedule social media posts for our product launch"

Agent Mode Process:
1. 📋 Reviews product launch details
2. 🎨 Generates multiple post variations
3. 📸 Selects appropriate images from brand library
4. 📱 Optimizes content for each platform (Twitter, LinkedIn, Instagram)
5. 📅 Schedules posts at optimal times
6. 🏷️ Adds relevant hashtags and mentions
7. 📊 Sets up performance tracking

User sees: "Created 12 social media posts across 3 platforms. Scheduled for optimal engagement times over the next 2 weeks."
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Documentation Updates: 

Non-Developer: "Update our product documentation based on the latest feature releases"

Agent Mode Process:
1. 📋 Reviews recent feature releases and changelogs
2. 📖 Analyzes existing documentation structure
3. ✏️ Identifies sections needing updates
4. 📝 Writes new content for added features
5. 🔗 Updates cross-references and links
6. 📸 Requests new screenshots where needed
7. 👀 Submits for review workflow

User sees: "Documentation updated for 5 new features. 3 sections revised, 2 new pages created. Submitted for review."
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3. Data Analysis and Insights

Business Intelligence: 

Non-Developer: "Analyze our customer data and provide insights for improving retention"

Agent Mode Process:
1. 📊 Connects to customer database and analytics tools
2. 🔍 Analyzes customer behavior patterns
3. 📈 Identifies retention trends and churn indicators
4. 🎯 Segments customers by behavior and value
5. 💡 Generates actionable insights and recommendations
6. 📋 Creates executive summary with key findings
7. 📅 Schedules follow-up analysis

User sees: "Customer retention analysis complete. Key finding: 23% churn rate among customers inactive for 30+ days. 5 recommendations provided for improvement."
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Market Research: 

Non-Developer: "Research our competitors' pricing strategies and market positioning"

Agent Mode Process:
1. 🔍 Identifies key competitors in the market
2. 🌐 Scrapes public pricing information
3. 📊 Analyzes pricing models and strategies
4. 📈 Compares features and positioning
5. 📝 Summarizes findings in structured report
6. 💡 Provides strategic recommendations
7. 📧 Delivers insights to stakeholders

User sees: "Competitor analysis complete. Reviewed 8 competitors. Our pricing is 15% above market average but offers 23% more features. Detailed report attached."
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4. Project Management and Coordination

Meeting Management: 

Non-Developer: "Organize and manage our weekly team meetings"

Agent Mode Process:
1. 📅 Checks team calendars for availability
2. 📧 Sends meeting invitations with agenda
3. 📋 Collects agenda items from team members
4. 📝 Prepares meeting materials and documents
5. ⏰ Sends reminders before the meeting
6. 📊 Takes notes and tracks action items
7. 📧 Distributes meeting summary and follow-ups

User sees: "Weekly team meeting scheduled for Thursday 2 PM. Agenda prepared with 6 items. 8 team members confirmed attendance."
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Task Automation: 

Non-Developer: "Automate our invoice processing workflow"

Agent Mode Process:
1. 📨 Monitors email for incoming invoices
2. 📄 Extracts data from invoice documents (OCR)
3. ✅ Validates invoice information against purchase orders
4. 💰 Checks budget availability
5. 📋 Routes for appropriate approvals
6. 💳 Processes payment when approved
7. 📊 Updates accounting system and reports

User sees: "Invoice processing automated. 12 invoices processed this week. Average processing time reduced from 3 days to 4 hours."
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Key Differences in Interaction Patterns

Developer (Ask Mode) Characteristics:

Query Style: 

- Specific technical questions
- Request for code examples
- Architecture guidance
- Debugging assistance
- Performance optimization
- Best practices inquiry
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Expected Output: 

- Detailed technical explanations
- Code snippets with comments
- Multiple solution options
- Trade-off analysis
- Implementation guidance
- Reference documentation
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User Control: 

- High level of control over AI responses
- Can iterate and refine queries
- Expects to implement solutions manually
- Values understanding over automation
- Prefers granular, detailed responses
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Non-Developer (Agent Mode) Characteristics:

Request Style: 

- Goal-oriented instructions
- Business process descriptions
- Outcome-focused requests
- Natural language commands
- High-level objectives
- Workflow automation needs
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Expected Output: 

- Completed tasks or processes
- Summary of actions taken
- Business-relevant insights
- Automated workflows
- Minimal technical details
- Results-focused reporting
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User Control: 

- Lower technical control, higher outcome control
- Delegates implementation to AI
- Values automation over understanding
- Prefers simple status updates
- Focuses on business results
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Hybrid Use Cases

1. Technical Non-Developers

DevOps Engineers: 

Hybrid Query: "Set up monitoring for our production environment and alert me about any issues"

Response combines both modes:
- Agent Mode: Automatically configures monitoring tools
- Ask Mode: Provides configuration details and customization options
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Data Analysts: 

Hybrid Query: "Analyze sales data and explain the statistical methods used"

Response combines both modes:
- Agent Mode: Performs the analysis and generates insights
- Ask Mode: Explains statistical methods and assumptions
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2. Business-Technical Bridge Roles

Product Managers: 

Hybrid Query: "Create a technical specification for the new feature and estimate development effort"

Response combines both modes:
- Agent Mode: Generates specification document
- Ask Mode: Provides technical implementation details and effort estimates
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Technical Writers: 

Hybrid Query: "Document this API and create user-friendly examples"

Response combines both modes:
- Agent Mode: Generates documentation structure and content
- Ask Mode: Provides technical accuracy and code examples
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Implementation Strategies

For Developer Teams:

Integration Approaches: 

IDE Integration:
  - VS Code extensions
  - IntelliJ plugins
  - Command-line tools
  - Git workflow integration

Development Workflow:
  - Code review assistance
  - Automated testing suggestions
  - Documentation generation
  - Refactoring recommendations

Knowledge Management:
  - Technical documentation
  - Code pattern libraries
  - Architecture decision records
  - Best practices databases
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For Non-Developer Teams:

Business Process Integration: 

Workflow Automation:
  - Email processing
  - Document generation
  - Data entry automation
  - Report creation

Communication Tools:
  - Slack/Teams integration
  - Meeting management
  - Project tracking
  - Status reporting

Business Intelligence:
  - Dashboard creation
  - Data analysis
  - Trend identification
  - Insight generation
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Measuring Success

Developer Success Metrics:

Productivity Indicators: 

- Code generation speed
- Bug resolution time
- Architecture decision quality
- Learning curve reduction
- Code review efficiency
- Documentation completeness
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Quality Measures: 

- Code quality scores
- Test coverage improvement
- Performance optimization results
- Security vulnerability reduction
- Maintainability metrics
- Technical debt reduction
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Non-Developer Success Metrics:

Efficiency Indicators: 

- Process automation rate
- Task completion time
- Error reduction percentage
- Workflow optimization
- Resource utilization
- Cost savings achieved
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Business Impact: 

- Customer satisfaction scores
- Revenue impact
- Operational efficiency
- Decision-making speed
- Compliance adherence
- Strategic goal achievement
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Future Evolution

Convergence Trends:

Unified Experiences: 

- Context-aware mode switching
- Skill-level adaptation
- Progressive disclosure of technical details
- Seamless handoff between modes
- Collaborative human-AI workflows
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Emerging Capabilities: 

- Multi-modal interactions (voice, visual, text)
- Real-time collaboration features
- Advanced reasoning capabilities
- Domain-specific specialization
- Continuous learning from user feedback
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Best Practices

For Developers Using Ask Mode:

  1. Be Specific: Provide context, constraints, and requirements
  2. Iterate: Refine queries based on initial responses
  3. Validate: Always review and test generated code
  4. Learn: Use responses as learning opportunities
  5. Document: Capture insights for future reference

For Non-Developers Using Agent Mode:

  1. Clear Objectives: Define desired outcomes clearly
  2. Provide Context: Share relevant business information
  3. Set Boundaries: Specify constraints and limitations
  4. Monitor Results: Review automated actions regularly
  5. Feedback Loop: Provide feedback to improve performance

Conclusion

The distinction between ask mode for developers and agent mode for non-developers represents a fundamental shift in how AI assistants can be optimized for different user needs. Developers benefit from detailed, technical responses that enhance their understanding and provide implementation guidance. Non-developers benefit from autonomous agents that can execute complex tasks with minimal technical input required.

Understanding these different interaction patterns enables organizations to:

  • Optimize AI deployment for different user groups
  • Maximize productivity gains across technical and non-technical teams
  • Reduce barriers to AI adoption by matching interfaces to user capabilities
  • Create more effective human-AI collaboration workflows
  • Scale AI benefits across the entire organization

As AI capabilities continue to evolve, the line between these modes may blur, but the fundamental principle remains: AI should adapt to users' needs, skills, and working styles rather than forcing users to adapt to the AI's limitations.

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