The Rise of the AI SDLC Best Better Practices for Adopting GenAI in Engineering Teams

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1. © 2024, Amazon Web Services, Inc. or its affiliates. All rights reserved. The Rise of the AI SDLC Best Better Practices for Adopting GenAI in Engineering Teams Lalit Kale AWS COMMUNITY DAY DUBLIN - 2025 Software Architect Globalization Partners
2. © 2024, Amazon Web Services, Inc. or its affiliates. All rights reserved. $whoami – Lalit Kale ⚫ Software Architect ⚫ Biases: AWS, AI as tool not a magic wand, Claude Code, EDA, DDD ⚫ First AWS Usage: 2006 ⚫ Dell → Verizon → Amdocs → Globalization Partners ⚫ 6 X AWS Community Builder
3. © 2024, Amazon Web Services, Inc. or its affiliates. All rights reserved. What we’ll discuss today • Why code generation > Organizational Context • The AI-SDLC Framework • Making It Real – Adoption Playbook • Measuring Success
4. © 2024, Amazon Web Services, Inc. or its affiliates. All rights reserved. The current Narrative is wrong X What Everyone Thinks AI Does • Autocomplete functions faster • Generate boilerplate code • Help developers type less • Individual productivity hack ✓ What AI Should Actually Do • Propagate organizational context • Enforce architectural standards • Eliminate knowledge silos • Organizational quality multiplier The Problem: Most teams don't fail because typing is slow The Reality: Teams fail due to vague requirements, inconsistent decisions, and tribal knowledge
5. © 2024, Amazon Web Services, Inc. or its affiliates. All rights reserved. Why Code generation is the least valuable AI Activity
6. © 2024, Amazon Web Services, Inc. or its affiliates. All rights reserved. "AI is a quality enforcement system, not a productivity hack ." Insight after org -wide deployment of Cursor /Claude Code
7. © 2024, Amazon Web Services, Inc. or its affiliates. All rights reserved. Traditional SDLC vs AI -SDLC Traditional SDLC • Context Loss: Requirements → Architecture → Code handoffs • Inconsistent Standards: Each team interprets design patterns differently • Tribal Knowledge: People Churn, Domain knowledge locked in someone’s heads • Manual Reviews • Documentation Drift: Code evolves, docs don't • Testing Gaps: Edge cases discovered in near production/ production ✓ AI-SDLC → Flow Engineering • Reduction in post -deployment defects • Decrease in architecture review cycles • Faster onboarding for new engineers • Consistency in applying design patterns • Automated Standards: ADRs enforced at every commit • Living Documentation: Auto -generated and always current THE BUSINESS IMPACT Organizational Silos gets amplified
8. © 2024, Amazon Web Services, Inc. or its affiliates. All rights reserved. The Flywheel Effect THE SECRET SAUCE – ORGANIZATIONAL CONTEXT Organizational Context Better Specs Better AI Suggestions Better Code Better Specs Why This Works • Specs are machine-readable: Unlike prose requirements, specs (OpenAPI, Terraform) are structured, versioned, and precise — perfect AI input. • Single source of truth: When specs define reality, AI generates consistent artifacts across all teams and services.
9. © 2024, Amazon Web Services, Inc. or its affiliates. All rights reserved. Organizational Context Pyramid In Practice Generated Artifacts • Output Layer (SDKs, Tests, Docs, Runbooks,) => Auto- generated always in sync with specs Intelligence Layer • AI Reasoning Layer (AWS Bedrock KG + Retrieval , Claude, Agents, Orchestration)→ Interprets context , enforces standards, generate artifacts Specifications Foundation • Context Layer (OpenAPI Specs, AsyncAPI, Event Schemas, ADRs, RFCs, Design Patterns, team topologies, risks catalogue) → Machine readable organizational knowledge Context is dynamic and fluid Automation AWS serverless for process orchestration Integration Points Tools for seamless workflow integration Agent Layer Claude with specialized sub-agents (AWS Bedrock) Retrieval Layer Semantic search for relevant context Context Store Versioned organizational knowledge repository (AWS S3)
10. © 2024, Amazon Web Services, Inc. or its affiliates. All rights reserved. Dumping Confluence = Context Window Flooding 1 1 china.org.cn
11. © 2024, Amazon Web Services, Inc. or its affiliates. All rights reserved. "Without the Org Context foundation, AI just autocompletes your existing chaos. Garbage in, garbage out. " Craft you r Organizational Context Layer. Focus on outcomes rather than output.
12. © 2024, Amazon Web Services, Inc. or its affiliates. All rights reserved. The road to context engineering is not a straight line
13. © 2024, Amazon Web Services, Inc. or its affiliates. All rights reserved. AI SDLC Requirements Architecture Iteration Planning Development Testing Deployment Ops and Support Product Manager Market Research Discovery Capabilities and Domain Analysis PRD Feature Creation Architect Feasibility User Scenarios Complexity Analysis Solution Design Quality Requirements ADR RFCs Engineering Manager Solution Validation Sprint Planning Epics Creation Epics complexity Analysis Epics Dependency Analysis Technical Lead User Stories JIRA Tasks Developer User Story ->Tasks Task --> Sub Tasks Code Developer Unit Tests Integration Tests Performance Tests Infra Developer Github Actions Workflow Release Notes Changelog Developer Observability Runbooks Humans Agents PM Agents PRD Agent Market Research Subagent Customer Interview Tasks Architect Agents Architecture Reviewer Database Architect Backend Architect Cloud Architect EM Agents Planning-Agent Sprint Prioritizer Epics complexity Analysis Epics Dependency Analysis TL Agents TaskMaster Code Reviewer Security Reviewer PR Reviewer Developer Agents TaskMaster DevOps/Infra Tasks Coding and Patterns Tasks Testing Tasks Performance Tests Tasks Security Auditor Infra Developer Agents Github Actions Workflow Release Notes Changelog Developer Agents Runbooks agent Activities & Artifacts Activities & Artifacts
14. © 2024, Amazon Web Services, Inc. or its affiliates. All rights reserved. Phase 1: Requirements Gathering Traditional Problems • Vague user stories open to interpretation • Missing edge cases and error scenarios • Inconsistent terminology across teams • Requirements drift during development AI-SDLC Solution • AI generates structured specs from prose • Validates completeness against patterns • Enforces organizational terminology • Specs become executable contracts FROM AMBIGUITY TO SPECIFICATIONS Claude Agent Role: Takes user story + organizational context → Generates OpenAPI spec with error codes, validation rules, and security requirements pre-filled from company standards.
15. © 2024, Amazon Web Services, Inc. or its affiliates. All rights reserved. Phase 2: Architecture & Design What the Architecture Agent Validates • Compliance with approved patterns (e.g., must use API Gateway + Lambda, not EC2) • Security posture (encryption at rest/in -transit, IAM least privilege) • Data residency and compliance requirements (GDPR, HIPAA) • Cost optimization (right -sized resources, reserved capacity) • Observability standards (logging, metrics, tracing) STANDARDS ENFORCEMENT AT DESIGN TIME Impact: Reduction in architecture review cycles — AI pre-validates designs before human review
16. © 2024, Amazon Web Services, Inc. or its affiliates. All rights reserved. Phase 3: Development Context Fed to AI • OpenAPI spec for current service • Related service APIs for integration • Approved design patterns for language • Security guidelines and examples • Past incident reports for edge cases AI-Generated Artifacts • Type -safe API client code • Input validation logic • Error handling patterns • Logging and monitoring hooks • Unit tests for edge cases CONTEXT - AWARE CODE GENERATION Key Difference: Code isn't generated from scratch — it's generated from organizational context. AI knows "this company uses Repository pattern + DDD, encrypts PII with KMS, and logs with structured JSON."
17. © 2024, Amazon Web Services, Inc. or its affiliates. All rights reserved. Phase 4: Testing What Makes This Different • Specs define test cases • OpenAPI spec with 3 error codes → AI generates 3 error test cases automatically • Historical learning • Past production incidents feed into test generation — "We had an outage when rate limit was exceeded, so always test rate limiting" • Contract testing • Ensures service integrations match their published specs • AI failure analysis • When tests fail, AI analyzes logs, suggests root cause, and proposes fixes SPEC - DRIVEN TEST GENERATION
18. © 2024, Amazon Web Services, Inc. or its affiliates. All rights reserved. Phase 5: Deployment AI-Generated Deployment Assets • Runbooks • Step -by-step deployment guide with rollback procedures • Health checks • Service -specific validation based on API spec • Monitoring dashboards • Auto -configured metrics and alerts • Rollback scripts • Automated recovery procedures Key Benefits • Zero -touch deployments • Automatic rollback on issues • Consistent deployment process • Built -in safety guardrails AUTOMATED, SAFE ROLLOUTS
19. © 2024, Amazon Web Services, Inc. or its affiliates. All rights reserved. Phase 6: Operations The Feedback Loop • This is where AI -SDLC becomes self -improving. • Every incident • Updates organizational context with failure patterns • Generates new test cases to prevent recurrence • Refines ADRs with lessons learned • Improves AI agent responses for future incidents INTELLIGENT MONITORING & INCIDENT RESPONSE Impact: faster mean time to recovery (MTTR) through AI-assisted incident response
20. © 2024, Amazon Web Services, Inc. or its affiliates. All rights reserved. Adoption Playbook • Inventory of all APIs with OpenAPI specs (or create them) • 10 -15 ADRs documenting key architecture decisions • Pattern library with 20 -30 code examples • Single pilot team using AI tools with organizational context THE PRACTICAL PATH FORWARD Critical Success Factor: Start narrow, prove value, scale culture. Don't try to transform everything at once. Pick one team, one project, measure results, then expand.
21. © 2024, Amazon Web Services, Inc. or its affiliates. All rights reserved. Measuring Success Wrong Metrics • Lines of code generated • Time saved typing • Number of AI suggestions accepted • Individual developer productivity ✓ Right Metrics • Post -deployment defect rate • Time from requirement to production • Architectural drift from ADRs • Consistency of pattern application • New engineer onboarding time • Mean time to recovery (MTTR) THE RIGHT METRICS These measure activity, not outcomes These measure quality and velocity
22. © 2024, Amazon Web Services, Inc. or its affiliates. All rights reserved. The Shift in Mindset Dimension Old Paradigm New Paradigm (AI-SDLC) AI Role Coding assistant Organizational knowledge system Focus Individual productivity Collective quality Goal Generate code faster Enforce standards consistently Foundation Code examples Machine-readable specs Success Metric Lines of code per day Post-deployment defects Knowledge Tribal, in people's heads Codified, in organizational context Quality Control Human review after coding AI enforcement during coding Documentation Manual, out of date Auto-generated, always current
23. © 2024, Amazon Web Services, Inc. or its affiliates. All rights reserved. Key Takeaways The real value is in propagating organizational context and enforcing standards consistently. 1. Code gen is the exhaust, not the engine Machine-readable specs (OpenAPI, Terraform, ADRs) are the foundation that makes everything else work. 2. Org context + specs = AI's true superpower From requirements to operations, AI ensures consistency and reduces cognitive load. 3. AI-SDLC - quality at every phase, not just coding. Invest in OpenAPI, IaC, ADRs, and pattern libraries before deploying AI tools. Fix the foundation first. 4. Start spec-driven development Track reduced defects and architectural consistency, not typing speed or lines of code. 5. Measure success by outcomes, not output.
24. © 2024, Amazon Web Services, Inc. or its affiliates. All rights reserved. The Core Shift AI-SDLC is not about replacing developers. It's about making organizational knowledge explicit, accessible, and actionable. Teams that win: Treat specs as products, context as infrastructure, and AI as the propagation mechanism. "The future belongs to organizations that codify their excellence and let AI propagate it."
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