Multi-Step Deployments
Progressive Deployment Pipeline
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Ask AI
You: "Deploy the new version to dev, then staging, then production - but only if tests pass at each stage"
Copilot:
1. Deploys to dev environment
2. Runs automated tests
3. If tests pass, deploys to staging
4. Runs staging tests
5. Waits for approval before production
6. Deploys to production
7. Monitors production health
Blue-Green Deployment
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Ask AI
You: "Set up a blue-green deployment for my-api in production"
Copilot:
1. Creates green environment (copy of blue)
2. Deploys new version to green
3. Runs health checks on green
4. Switches traffic to green
5. Monitors for issues
6. Keeps blue as backup
Canary Deployment
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Ask AI
You: "Deploy version 2.0 as a canary to 10% of production traffic"
Copilot:
1. Deploys new version alongside current
2. Configures traffic split (90% old, 10% new)
3. Monitors error rates
4. Gradually increases traffic if healthy
5. Rolls back if errors spike
Complex Infrastructure Changes
Multi-Environment Setup
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Ask AI
You: "Create a complete staging infrastructure that mirrors production but with half the resources"
Copilot:
1. Analyzes production infrastructure
2. Creates staging environment
3. Clones all services
4. Reduces resource allocations by 50%
5. Adjusts database sizes
6. Configures networking
7. Sets up monitoring
8. Validates complete setup
Infrastructure Migration
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Ask AI
You: "Migrate all staging environments from cluster-east to cluster-west"
Copilot:
1. Lists all staging environments
2. For each environment:
- Creates new environment in cluster-west
- Deploys all services
- Migrates data
- Validates functionality
- Switches DNS/routing
- Deletes old environment
3. Reports migration summary
Time-Based Automation
Scheduled One-Time Tasks
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Ask AI
You: "Stop all development environments tonight at 8 PM"
Copilot:
1. Identifies all development environments
2. Schedules shutdown task for 8 PM
3. Confirms scheduled action
4. Executes at specified time
5. Sends notification when complete
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Ask AI
You: "Deploy the hotfix to production tomorrow at 2 AM"
Copilot:
1. Validates hotfix is ready
2. Schedules deployment for 2 AM
3. Sets up pre-deployment checks
4. Executes deployment at scheduled time
5. Monitors post-deployment health
6. Alerts team of completion
Recurring Scheduled Tasks
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Ask AI
You: "Every weekday at 6 PM, stop all dev and staging environments"
Copilot:
1. Creates recurring schedule (Mon-Fri at 6 PM)
2. Identifies target environments
3. Configures automatic shutdown
4. Sends daily summary reports
5. Maintains schedule until cancelled
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Ask AI
You: "Start all development environments every morning at 8 AM on weekdays"
Copilot:
1. Sets up recurring schedule (Mon-Fri at 8 AM)
2. Lists all dev environments
3. Configures automatic startup
4. Ensures environments are ready by 8:30 AM
5. Notifies team when environments are available
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Ask AI
You: "Run database backups every night at 2 AM and keep for 7 days"
Copilot:
1. Creates nightly backup schedule (2 AM daily)
2. Configures backup retention (7 days)
3. Sets up backup verification
4. Monitors backup success/failure
5. Alerts on backup issues
6. Manages automatic cleanup of old backups
Time-Based Cost Optimization
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Ask AI
You: "Automatically scale down all non-production services to minimum replicas on weekends"
Copilot:
1. Identifies non-production environments
2. Creates weekend schedule (Fri 6 PM to Mon 8 AM)
3. Records current replica counts
4. Scales to minimum on Friday evening
5. Restores original counts Monday morning
6. Reports weekly cost savings
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Ask AI
You: "During business hours (9 AM - 6 PM), ensure production has 5 replicas. Outside hours, scale to 2 replicas."
Copilot:
1. Sets up two recurring schedules
2. Scale up schedule: Weekdays 9 AM (to 5 replicas)
3. Scale down schedule: Weekdays 6 PM (to 2 replicas)
4. Monitors application performance during transitions
5. Tracks cost savings from reduced off-hours capacity
Maintenance Windows
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Ask AI
You: "Every Sunday from 2 AM to 4 AM, perform maintenance on staging: update all services, run database optimization, clear caches"
Copilot:
1. Creates weekly maintenance window (Sun 2-4 AM)
2. Schedules sequential tasks:
- 2:00 AM: Deploy latest versions to all services
- 2:30 AM: Run database optimization scripts
- 3:00 AM: Clear application caches
- 3:30 AM: Run health checks
3. Monitors each task completion
4. Sends maintenance report at 4 AM
5. Alerts if any task fails
Conditional Time-Based Actions
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Ask AI
You: "If CPU usage stays above 80% for more than 30 minutes during business hours, scale up. After 6 PM, just alert me instead."
Copilot:
1. Sets up time-based conditional rules:
- Business hours (9 AM - 6 PM): Auto-scale on high CPU
- After hours (6 PM - 9 AM): Alert only
2. Monitors CPU metrics continuously
3. During business hours:
- Detects sustained high CPU
- Automatically scales up
- Notifies of action taken
4. After hours:
- Detects sustained high CPU
- Sends alert without scaling
- Waits for manual intervention
Ephemeral Environment Lifecycle
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Ask AI
You: "Create a preview environment for PR #123 and automatically delete it in 7 days or when the PR is merged"
Copilot:
1. Creates preview environment for PR #123
2. Deploys PR branch
3. Sets up two deletion triggers:
- Time-based: Auto-delete after 7 days
- Event-based: Auto-delete on PR merge
4. Monitors PR status
5. Deletes environment when either condition is met
6. Notifies team of deletion
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Ask AI
You: "Every Monday, delete all feature environments older than 2 weeks"
Copilot:
1. Creates weekly cleanup schedule (Mondays)
2. Scans for feature environments
3. Identifies environments older than 2 weeks
4. Checks for recent activity
5. Deletes inactive old environments
6. Sends weekly cleanup report
7. Reports cost savings from cleanup
Automated Optimization
Cost Optimization Workflow
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Ask AI
You: "Analyze my infrastructure and implement cost savings where possible"
Copilot:
1. Analyzes all environments
2. Identifies underutilized resources
3. Finds idle services
4. Suggests optimizations:
- Right-size over-provisioned services
- Stop inactive environments
- Use spot instances where appropriate
- Consolidate low-traffic services
5. Implements approved optimizations
6. Reports projected monthly savings
Performance Optimization
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Ask AI
You: "Optimize the performance of my production stack"
Copilot:
1. Analyzes performance metrics
2. Identifies bottlenecks
3. Reviews configurations
4. Suggests improvements:
- Increase resources for constrained services
- Optimize database queries
- Add caching layers
- Adjust auto-scaling rules
5. Implements approved changes
6. Monitors impact
Disaster Recovery
Automated Backup and Restore
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Ask AI
You: "Backup production, make breaking changes to staging, then restore if needed"
Copilot:
1. Takes snapshot of production state
2. Applies changes to staging
3. Monitors for issues
4. If problems detected:
- Automatically restores from snapshot
- Documents what went wrong
- Suggests fixes
Multi-Region Failover
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Ask AI
You: "If production goes down, automatically failover to DR region"
Copilot:
1. Monitors production health
2. If failure detected:
- Validates DR environment is healthy
- Switches DNS to DR region
- Notifies team
- Provides status dashboard
3. When production recovers:
- Syncs data
- Fails back to primary
Batch Operations
Bulk Environment Management
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Ask AI
You: "For all feature branches older than 2 weeks: stop them if inactive, delete them if older than 1 month"
Copilot:
1. Lists all feature branch environments
2. Checks last activity for each
3. Categorizes by age and activity
4. Stops inactive 2-week-old environments
5. Deletes 1-month-old environments
6. Generates cleanup report
7. Calculates cost savings
Mass Configuration Updates
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Ask AI
You: "Update the API_VERSION environment variable to 2.0 across all environments"
Copilot:
1. Lists all environments
2. For each environment:
- Finds services with API_VERSION
- Updates value to 2.0
- Triggers redeployment
- Waits for healthy status
3. Reports success/failures
CI/CD Integration
Automated Release Pipeline
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Ask AI
You: "When a new tag is pushed, automatically deploy to staging, run tests, and notify Slack"
Copilot:
1. Sets up webhook for new tags
2. On tag event:
- Deploys to staging
- Waits for deployment
- Runs integration tests
- If tests pass:
- Notifies Slack with success
- Creates production deployment plan
- If tests fail:
- Rolls back staging
- Notifies Slack with failure details
- Logs for review
Hotfix Workflow
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Ask AI
You: "I need to deploy a hotfix immediately - bypass normal pipeline"
Copilot:
1. Identifies hotfix branch
2. Creates emergency deployment plan
3. Deploys directly to production
4. Monitors closely for issues
5. Documents hotfix deployment
6. Reminds to backport to main
Data Migration
Zero-Downtime Migration
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Ask AI
You: "Migrate the database from PostgreSQL 13 to 15 with zero downtime"
Copilot:
1. Creates new PostgreSQL 15 database
2. Sets up replication from old to new
3. Monitors replication lag
4. When in sync:
- Switches application to new database
- Monitors for errors
- Keeps old database as backup
5. After validation period:
- Removes old database
- Updates documentation
Data Archival
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Ask AI
You: "Archive all data from environments deleted more than 3 months ago"
Copilot:
1. Identifies deleted environments
2. Locates associated data
3. Creates archive storage
4. Exports data to archive
5. Verifies archive integrity
6. Removes from active storage
7. Updates inventory
Compliance & Auditing
Compliance Scan
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Ask AI
You: "Audit all production services for security compliance"
Copilot:
1. Scans all production services
2. Checks:
- Environment variables not hardcoded
- No public database access
- HTTPS enforced
- Secrets properly managed
- Latest security patches applied
3. Generates compliance report
4. Flags violations
5. Suggests remediations
Access Review
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Ask AI
You: "Review who has access to production and ensure least privilege"
Copilot:
1. Lists all users with production access
2. Reviews access levels
3. Identifies overly permissive access
4. Suggests access reductions
5. Generates access audit report
Monitoring & Alerting
Proactive Issue Detection
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Ask AI
You: "Monitor production and automatically fix common issues"
Copilot:
1. Monitors production continuously
2. Detects patterns like:
- Memory leaks (restart affected services)
- Connection pool exhaustion (scale up)
- Failed health checks (redeploy)
3. Automatically remediates known issues
4. Notifies of actions taken
5. Escalates unknown issues
Intelligent Alerting
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Ask AI
You: "Alert me only for critical issues, auto-handle everything else"
Copilot:
1. Monitors all infrastructure
2. Categorizes issues by severity
3. Auto-remediates:
- Low: Service restarts, scaling adjustments
- Medium: Failovers, traffic rerouting
4. Alerts for:
- High: Data corruption, security issues
- Critical: Complete outages, data breaches
5. Provides context and suggested actions
Best Practices for Complex Workflows
Break Down Complex Requests
Break Down Complex Requests
For very complex workflows, break them into phases:
- “First, set up the infrastructure…”
- “Next, configure the services…”
- “Finally, validate everything…”
Use Checkpoints
Use Checkpoints
Request confirmation at critical points:“Deploy to staging, wait for my approval, then continue to production”
Define Success Criteria
Define Success Criteria
Be explicit about what success looks like:“Deploy and consider it successful only if error rate stays below 0.1%”
Plan for Failures
Plan for Failures
Include failure handling in your requests:“Try to deploy, but if it fails, automatically rollback and notify me”