Deployment Workflow

This document describes the deployment workflow in the Engineering AI Agent system, focusing on how code changes move from development to production environments.

Overview

The deployment workflow encompasses several stages:

Environment preparation
Build process
Testing in staging
Production deployment
Monitoring and rollback procedures

Workflow Stages

1. Environment Preparation

Primary Roles: RD (Research & Development), SRE (Site Reliability Engineering)

Before deployment, the environment is prepared:

Configuration files are updated for the target environment
Environment variables are set appropriately
Infrastructure is provisioned if needed
Dependencies are updated to required versions

Example environment configuration:

# config/environment/staging.yaml
environment: staging
logging:
  level: info
  format: json
database:
  host: ${DB_HOST}
  port: ${DB_PORT}
  name: engineering_agent_staging
  pool_size: 10
services:
  slack:
    enabled: true
  github:
    enabled: true
  jira:
    enabled: true
  clickup:
    enabled: false

2. Build Process

Primary Role: CI/CD Pipeline

The build process prepares the application for deployment:

Source code is fetched from the repository
Dependencies are installed
Code is compiled or bundled if necessary
Tests are run to verify functionality
Artifacts are packaged for deployment

Example build workflow:

name: Build

on:
  push:
    branches: [main, develop]
  pull_request:
    branches: [main]

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v3
    - name: Set up Python
      uses: actions/setup-python@v4
      with:
        python-version: '3.10'
    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install -r requirements.txt
        pip install -r requirements-dev.txt
    - name: Run tests
      run: pytest
    - name: Build package
      run: |
        pip install build
        python -m build
    - name: Upload artifact
      uses: actions/upload-artifact@v3
      with:
        name: dist
        path: dist/

3. Testing in Staging

Primary Roles: QA (Quality Assurance), SRE (Site Reliability Engineering)

Before production deployment, changes are tested in a staging environment:

Built artifacts are deployed to the staging environment
Integration with external services is verified
Performance and security testing is performed
User acceptance testing may be conducted
Smoke tests verify basic functionality

Example staging deployment workflow:

name: Deploy to Staging

on:
  workflow_run:
    workflows: ["Build"]
    branches: [develop]
    types: [completed]

jobs:
  deploy-staging:
    runs-on: ubuntu-latest
    if: ${{ github.event.workflow_run.conclusion == 'success' }}
    steps:
    - name: Download artifact
      uses: actions/download-artifact@v3
      with:
        name: dist
        path: dist/
    - name: Deploy to staging
      env:
        DEPLOY_TOKEN: ${{ secrets.STAGING_DEPLOY_TOKEN }}
      run: |
        ./scripts/deploy.sh --env staging --token $DEPLOY_TOKEN
    - name: Run smoke tests
      run: |
        ./scripts/smoke_tests.sh --env staging

4. Production Deployment

Primary Roles: SRE (Site Reliability Engineering), RD (Research & Development)

Once verified in staging, changes are deployed to production:

Deployment may follow different strategies (blue/green, canary, etc.)
Database migrations are applied if necessary
Configuration is updated for production
Traffic is gradually shifted to the new version
Deployment is monitored for issues

Example production deployment workflow:

name: Deploy to Production

on:
  workflow_dispatch:
    inputs:
      version:
        description: 'Version to deploy'
        required: true
      strategy:
        description: 'Deployment strategy'
        required: true
        default: 'blue-green'
        type: choice
        options:
        - blue-green
        - canary
        - rolling

jobs:
  deploy-production:
    runs-on: ubuntu-latest
    steps:
    - name: Checkout deploy scripts
      uses: actions/checkout@v3
      with:
        sparse-checkout: scripts/
    - name: Deploy to production
      env:
        DEPLOY_TOKEN: ${{ secrets.PROD_DEPLOY_TOKEN }}
        VERSION: ${{ github.event.inputs.version }}
        STRATEGY: ${{ github.event.inputs.strategy }}
      run: |
        ./scripts/deploy.sh --env production --token $DEPLOY_TOKEN --version $VERSION --strategy $STRATEGY
    - name: Verify deployment
      run: |
        ./scripts/verify_deployment.sh --env production

5. Monitoring and Rollback

Primary Role: SRE (Site Reliability Engineering)

After deployment, the system is monitored:

Performance metrics are tracked
Error rates are monitored
User-impacting issues are identified
Rollback procedures are executed if necessary

Example monitoring alert configuration:

# prometheus-alerts.yaml
groups:
- name: engineering_agent_alerts
  rules:
  - alert: HighErrorRate
    expr: sum(rate(http_requests_total{status=~"5.."}[5m])) / sum(rate(http_requests_total[5m])) > 0.05
    for: 2m
    labels:
      severity: critical
    annotations:
      summary: High error rate detected
      description: Error rate exceeded 5% for more than 2 minutes

  - alert: SlowResponses
    expr: http_request_duration_seconds{quantile="0.9"} > 2
    for: 5m
    labels:
      severity: warning
    annotations:
      summary: Slow API responses detected
      description: 90th percentile response time is above 2 seconds for 5 minutes

Deployment Strategies

Blue-Green Deployment

Two identical environments are maintained (Blue and Green)
Changes are deployed to the inactive environment
Tests verify the new deployment
Traffic is switched from the active to the inactive environment
Allows for immediate rollback by switching traffic back

Canary Deployment

New version is deployed alongside the old version
Small percentage of traffic is routed to the new version
Traffic percentage gradually increases if no issues are found
Allows for early detection of issues with minimal impact
Rollback requires scaling down the new version

Rolling Deployment

New version is deployed gradually across instances
One or more instances are updated at a time
Healthchecks verify each updated instance
Process continues until all instances are updated
Balances deployment speed with risk mitigation

Versioning and Release Management

Semantic Versioning

The system follows semantic versioning (SemVer):

Format: MAJOR.MINOR.PATCH (e.g., 1.2.3)
MAJOR: Incompatible API changes
MINOR: New features (backward compatible)
PATCH: Bug fixes (backward compatible)

Release Branching Strategy

main branch represents the current production state
develop branch is used for ongoing development
Feature branches branch off from and merge back to develop
Release branches are created from develop
Hotfixes branch directly from main and merge to both main and develop

Release Process

Create a release branch (release/vX.Y.Z) from develop
Perform final testing and bug fixes in the release branch
Merge the release branch to main
Tag the release in main (vX.Y.Z)
Merge the release branch back to develop

Best Practices

Configuration Management

Environment-specific configuration remains separate from code
Sensitive values are stored in secure vaults
Configuration is versioned and auditable
Changes to configuration follow the same review process as code

Database Changes

Database migrations are automated
Migrations are backward compatible when possible
Large data migrations are performed in batches
Rollback procedures are defined for database changes

Deployment Safety

Feature flags control functionality exposure
Circuit breakers prevent cascading failures
Rate limiting protects against unexpected load
Automated rollbacks trigger on error thresholds

Documentation and Communication

Release notes document all changes
Stakeholders are notified before significant deployments
Post-deployment status is communicated to the team
Deployment schedules respect low-traffic periods

Example Workflow

Release Planning:
- Features are prioritized for the next release
- Version number is assigned based on changes
- Release branch is created from develop
Preparation:
- Final testing is performed in the release branch
- Release notes are prepared
- Deployment plan is reviewed
Staging Deployment:
- Release is deployed to staging environment
- Integration and acceptance testing is performed
- Issues are fixed directly in the release branch
Production Deployment:
- Stakeholders are notified of the upcoming deployment
- Deployment is scheduled during a low-traffic period
- Release is deployed following the chosen strategy
Post-Deployment:
- System is monitored for anomalies
- Success or issues are communicated to the team
- Lessons learned are documented for future deployments

Overview​

Workflow Stages​

1. Environment Preparation​

2. Build Process​

3. Testing in Staging​

4. Production Deployment​

5. Monitoring and Rollback​

Deployment Strategies​

Blue-Green Deployment​

Canary Deployment​

Rolling Deployment​

Versioning and Release Management​

Semantic Versioning​

Release Branching Strategy​

Release Process​

Best Practices​

Configuration Management​

Database Changes​

Deployment Safety​

Documentation and Communication​

Example Workflow​