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AASDLC Workflow

A Comprehensive Guide to Agent-Assisted Software Development

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Overview

The AASDLC workflow provides a structured approach to software development using AI agents as active participants. This methodology spans from initial conception through continuous maintenance, ensuring quality, efficiency, and consistent delivery.

🔧 Process Rules

  1. Phase Structure: The workflow is organized into distinct phases that group related steps
  2. Step Components: Each step defines:
    • Actors: Who is involved (humans, AI agents, or both)
    • Reference Sources: Documentation, standards, or information sources consulted
    • Tooling: Generic tool categories with specific examples
    • Artifacts: Deliverables produced with clear acceptance criteria
  3. Tooling Convention: Tools are listed generically with brand-specific examples (e.g., "Enterprise Chat Application: Slack/MS Teams") with required features explicitly called out
  4. AI Agent Capabilities: When AI agents are specified as actors, their required capabilities must be documented
  5. Acceptance Criteria: All artifacts must have measurable, verifiable acceptance criteria
  6. Continuous Integration: Steps may occur in parallel or iterate as needed within phase boundaries

Phase 1: Conception & Idea Refinement

Transform business ideas into well-defined, feasible technical specifications with AI-assisted discovery and design.

Step 1.1: Initial Discovery Session

👥 Actors
Business Stakeholder(s) Technical Lead AI Meeting Agent

AI Capabilities: Real-time transcription, active listening, question generation, document creation/editing, requirement gap analysis

🛠️ Key Tools
Slack/MS Teams Fireflies.ai/Otter.ai Confluence/Notion
📋 Artifacts
Meeting Recording & Transcript
✓ 95%+ transcription accuracy with speaker identification and timestamps
Initial Requirements Document
✓ Business objectives, success metrics, stakeholders, scope boundaries, and feature priorities defined
Question Log
✓ All clarifying questions documented with context

Step 1.2: Requirement Refinement & Gap Analysis

👥 Actors
Technical Lead AI Analysis Agent

AI Capabilities: Requirement analysis, gap detection, consistency checking, standards compliance validation

🛠️ Key Tools
GitHub Copilot/Cline/Cursor Jira/Azure DevOps
📋 Artifacts
Refined Requirements Document
✓ SMART requirements with technical feasibility, dependencies, risks, and compliance requirements defined
Technical Constraints Document
✓ Technology stack, integrations, performance, scalability, and security controls documented

Step 1.3: Design & Prototyping

👥 Actors
Technical Lead UX/UI Designer (part-time) AI Design Agent

AI Capabilities: Mockup generation, design system application, accessibility compliance checking, multi-variant generation

🛠️ Key Tools
v0.dev/Galileo AI Figma/Adobe XD
📋 Artifacts
UI/UX Mockups (5-10 variations)
✓ Responsive designs with accessibility compliance and stakeholder approval
Interactive Prototype
✓ Clickable key user journeys with navigation flow
Design Specification Document
✓ Component specifications, typography, colors, and interaction patterns defined

Step 1.4: Architecture & Technical Planning

👥 Actors
Technical Lead AI Architecture Agent

AI Capabilities: Architecture diagram generation, technology stack recommendations, cost estimation, performance modeling

🛠️ Key Tools
Mermaid/Lucidchart AWS/Azure/GCP Calculator
📋 Artifacts
System Architecture Diagram
✓ All components, data flow, integration points, and security boundaries documented
Technology Stack Document
✓ All technologies specified with versions, rationale, licensing, and security assessment
Cost Estimate
✓ Development, infrastructure, and maintenance costs with 20% contingency buffer
Project Timeline
✓ Phases, milestones, dependencies, and risk buffers with stakeholder approval

Phase 2: Development

Build the application with AI-collaborative development, leveraging AI agents for code generation, testing, and integration.

Step 2.1: Development Environment Setup

👥 Actors
Technical Lead DevOps Engineer (part-time) AI Infrastructure Agent
🛠️ Key Tools
Git (GitHub/GitLab) Docker/Docker Compose GitHub Actions/GitLab CI Terraform/CloudFormation
📋 Artifacts
Repository Structure
✓ Standard structure with README, .gitignore, LICENSE, and CONTRIBUTING guide
Development Environment Configuration
✓ Developers can run locally within 15 minutes
CI/CD Pipeline Configuration
✓ Automated build, tests, quality checks, and deployment to dev environment

Step 2.2: Core Development - Backend

👥 Actors
Technical Lead AI Development Agent

AI Capabilities: Code generation, API creation, database schema generation, unit test generation, code review

🛠️ Key Tools
GitHub Copilot/Cline/Cursor Postman/Insomnia Database IDE Tools SonarQube/CodeClimate
📋 Artifacts
Backend Application Code
✓ All API endpoints with validation, error handling, logging, and OWASP Top 10 addressed
Database Schema & Migrations
✓ Normalized schema with migrations, rollback scripts, indexes, and constraints
API Documentation
✓ All endpoints documented with examples (Swagger/OpenAPI)
Unit Tests
✓ 85%+ code coverage, all critical paths tested, tests run < 5 minutes

Step 2.3: Core Development - Frontend

👥 Actors
Technical Lead AI Development Agent

AI Capabilities: Component generation, responsive layout implementation, state management, accessibility implementation, test generation

🛠️ Key Tools
React/Vue/Angular Jest/Vitest Storybook
📋 Artifacts
Frontend Application Code
✓ Responsive, accessible (WCAG 2.1 AA), performant (Core Web Vitals), with all screens implemented
Component Library/Storybook
✓ All reusable components documented with variations and visual regression testing
Frontend Unit & Component Tests
✓ 80%+ code coverage with accessibility tests, runs < 3 minutes

Step 2.4: Integration Development

👥 Actors
Technical Lead AI Development Agent

AI Capabilities: Integration code generation, API client generation, error handling, integration test generation

📋 Artifacts
Integration Code
✓ All external integrations with retry logic, circuit breakers, timeout handling, and secure secret management
Integration Tests
✓ All integration points tested including error scenarios with mocks available

Phase 3: Testing

Comprehensive testing with AI-generated test suites, ensuring quality, performance, and security.

Step 3.1: Automated Test Suite Generation

👥 Actors
Technical Lead AI Testing Agent

AI Capabilities: Test scenario generation, Gherkin/BDD writing, edge case identification, test data generation, coverage analysis

🛠️ Key Tools
Cucumber/SpecFlow/Behave Jest/Pytest/JUnit Coverage Tools
📋 Artifacts
Gherkin Feature Files
✓ All major features in plain language, business-stakeholder validated, executable scenarios
Comprehensive Test Suite
✓ 85%+ unit coverage, all API endpoints, all UI components, all critical user journeys, runs < 10 minutes
Test Data Sets
✓ Representative data for all scenarios including edge cases and invalid data

Step 3.2: Automated UI/E2E Testing

👥 Actors
Technical Lead AI Testing Agent
🛠️ Key Tools
Playwright/Cypress Percy/Chromatic axe-core/Lighthouse
📋 Artifacts
E2E Test Suite
✓ All critical paths automated, multi-browser, multi-viewport, < 1% flake rate
Visual Regression Test Suite
✓ Baselines for all major screens with responsive sizes covered
Accessibility Test Report
✓ WCAG 2.1 AA compliance with keyboard navigation and screen reader testing

Step 3.3: Performance & Load Testing

👥 Actors
Technical Lead DevOps Engineer (part-time) AI Testing Agent
🛠️ Key Tools
k6/JMeter/Gatling New Relic/Datadog
📋 Artifacts
Performance Test Suite
✓ Load, stress (2x), endurance (24hr), and spike tests documented
Performance Baseline Report
✓ Response times (p50, p95, p99), throughput, resource utilization, and bottlenecks documented

Step 3.4: Security Testing

👥 Actors
Technical Lead Security Engineer (part-time) AI Security Agent
🛠️ Key Tools
SonarQube/Snyk OWASP ZAP Dependabot GitGuardian
📋 Artifacts
Security Test Results
✓ SAST and DAST scans clean, dependencies addressed, no secrets in code
Security Compliance Report
✓ Compliance requirements mapped with evidence and security team sign-off
Threat Model Document
✓ Assets, threats, mitigations, and accepted residual risks documented

Phase 4: Deployment

Automated deployment pipeline with infrastructure as code and progressive delivery strategies.

Step 4.1: Deployment Environment Setup

👥 Actors
DevOps Engineer (part-time) Technical Lead AI Infrastructure Agent
🛠️ Key Tools
Terraform/CloudFormation Kubernetes/ECS AWS/Azure/GCP
📋 Artifacts
Infrastructure Code
✓ All infrastructure as code with isolated environments, security configured, monitoring, backups, and cost tags
Deployment Runbooks
✓ Step-by-step deployment, rollback, troubleshooting, and disaster recovery procedures
Environment Configuration
✓ All variables documented, secrets in secure vault, configuration validated per environment

Step 4.2: Continuous Deployment Pipeline

👥 Actors
DevOps Engineer (part-time) Technical Lead AI DevOps Agent
🛠️ Key Tools
GitHub Actions/GitLab CI Docker Hub/ECR/ACR Argo Rollouts/Flagger
📋 Artifacts
Deployment Pipeline Configuration
✓ Multi-stage pipeline with automated dev deploy, approval gates for staging/production, automated rollback
Deployment Strategy Documentation
✓ Deployment method (blue-green/canary), schedule, maintenance windows, and success criteria defined

Step 4.3: Production Deployment

👥 Actors
Technical Lead DevOps Engineer (part-time) Business Stakeholder (approval) AI Monitoring Agent
📋 Artifacts
Production Deployment Record
✓ Time, version, executor, approvals, and stakeholder communication documented
Deployment Verification Report
✓ Health checks passing, smoke tests passing, no critical errors, stable metrics, stakeholder sign-off

Phase 5: Verification

Comprehensive monitoring, user acceptance testing, and production validation to ensure quality delivery.

Step 5.1: Monitoring & Observability Setup

👥 Actors
DevOps Engineer (part-time) Technical Lead AI Monitoring Agent
🛠️ Key Tools
New Relic/Datadog ELK/Splunk/CloudWatch Prometheus/Grafana Sentry/Rollbar Pingdom/UptimeRobot
📋 Artifacts
Monitoring Dashboards
✓ System health, application performance, and business metrics dashboards with real-time updates
Alert Configuration
✓ Critical alerts, performance alerts, error rate alerts with routing and escalation procedures
Logging Configuration
✓ Centralized logs with retention policy, sensitive data redacted, queries documented
SLO/SLA Documentation
✓ Service Level Objectives, indicators, error budgets, and stakeholder agreement

Step 5.2: User Acceptance Testing (UAT)

👥 Actors
Business Stakeholder(s) End Users (selected) Technical Lead (support) AI Testing Agent
🛠️ Key Tools
TestRail/Zephyr Jira/Azure DevOps UserTesting/Hotjar
📋 Artifacts
UAT Test Cases
✓ All Gherkin scenarios translated to business-user-friendly test cases
UAT Results Report
✓ All tests executed with pass/fail status, issues documented, feedback collected, acceptance decision
Issue Log
✓ All issues with reproduction steps, prioritized, critical issues resolved, remaining issues accepted

Step 5.3: Production Validation

👥 Actors
Technical Lead DevOps Engineer (part-time) AI Validation Agent
🛠️ Key Tools
Datadog/New Relic Synthetics Google Analytics/Mixpanel
📋 Artifacts
Production Validation Test Results
✓ All critical journeys tested, health checks passing, no critical errors, performance within range
Production Metrics Report
✓ Baseline metrics (24hr), comparison to pre-deployment, anomalies investigated, business metrics tracking
Go-Live Communication
✓ Stakeholders notified, customers informed, support team briefed, documentation updated

Phase 6: Maintenance & Continuous Improvement

Ongoing monitoring, optimization, feature iteration, and security maintenance to ensure long-term success.

Step 6.1: Ongoing Monitoring & Support

👥 Actors
Technical Lead (on-call) DevOps Engineer (on-call) AI Operations Agent

AI Capabilities: Log analysis, incident triage, root cause analysis, automated remediation suggestions

🛠️ Key Tools
PagerDuty/Opsgenie Statuspage.io
📋 Artifacts
On-Call Schedule
✓ 24/7 coverage with fair rotation, escalation paths, and accessible documentation
Incident Log
✓ All incidents with severity, response time, resolution time, and post-incident reviews
System Health Reports (Weekly)
✓ Uptime, performance trends, error rates, business metrics, incidents summary, action items

Step 6.2: Performance Optimization

👥 Actors
Technical Lead AI Optimization Agent

AI Capabilities: Bottleneck analysis, code optimization, query optimization, cost optimization

📋 Artifacts
Performance Optimization Backlog
✓ Issues prioritized by impact with effort estimates and quarterly roadmap
Optimization Implementation Report
✓ Changes documented with before/after metrics, cost impact, and lessons learned

Step 6.3: Feature Iteration & Enhancement

👥 Actors
Business Stakeholder(s) Technical Lead AI Product Agent

AI Capabilities: Feedback analysis, feature prioritization, A/B test design, impact prediction

🛠️ Key Tools
Google Analytics/Mixpanel Optimizely/LaunchDarkly Hotjar/FullStory ProductBoard/Aha!
📋 Artifacts
Product Feedback Analysis
✓ Feedback categorized by theme with analytics, pain points, opportunities, and competitive gaps
Feature Roadmap
✓ Quarterly plan with prioritized features (RICE/ICE), dependencies, resource estimates, stakeholder buy-in
A/B Test Results
✓ Hypothesis, design, statistical significance, winner determination, learnings, and next steps documented

Step 6.4: Security & Compliance Maintenance

👥 Actors
Technical Lead Security Engineer (part-time) AI Security Agent
🛠️ Key Tools
Snyk/Dependabot Vanta/Drata Splunk/Azure Sentinel
📋 Artifacts
Security Patch Log
✓ All vulnerabilities tracked, prioritized, patched within SLA (7-30 days for critical/high)
Compliance Audit Reports (Annual/Semi-annual)
✓ All controls tested, evidence provided, gaps remediated, audit sign-off, certification maintained
Security Training Completion (Annual)
✓ All team members trained with certificates, secure coding refreshed, incident procedures reviewed

Step 6.5: Documentation Maintenance

👥 Actors
Technical Lead AI Documentation Agent

AI Capabilities: Documentation generation from code, gap identification, updates automation, quality analysis

🛠️ Key Tools
GitBook/Docusaurus Swagger UI/Redoc JSDoc/Sphinx
📋 Artifacts
Up-to-Date Documentation
✓ Architecture, API, deployment, onboarding, and user docs current, reviewed quarterly minimum
Documentation Health Report (Quarterly)
✓ Outdated docs identified, gaps identified, usage metrics included, improvement plan established
Knowledge Base
✓ Common issues, troubleshooting guides, FAQs maintained with effective search and regular updates

Success Metrics

📈 Development Velocity

  • Features delivered per quarter
  • Lead time (idea to production)
  • Cycle time (dev start to production)
  • Deployment frequency

✅ Quality Metrics

  • Test coverage percentage
  • Bug escape rate (production bugs)
  • Mean time to detect (MTTD)
  • Mean time to resolve (MTTR)

⚙️ Operational Metrics

  • System uptime percentage
  • Performance (response time p95/p99)
  • Error rate
  • Incident count and severity

💼 Business Metrics

  • User satisfaction (NPS, CSAT)
  • Feature adoption rate
  • Time to value
  • Cost per feature

🤖 AI Effectiveness Metrics

  • AI code generation usage %
  • AI-generated code review pass rate
  • AI testing coverage contribution
  • Time saved by AI automation

Ready to Implement AASDLC?

This workflow provides a comprehensive framework for AI-assisted software development. Adapt it to your organization's needs and start accelerating your delivery today.

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