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
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
rights reserved. What we’ll discuss today • Why code generation > Organizational Context • The AI-SDLC Framework • Making It Real – Adoption Playbook • Measuring Success
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
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
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.
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.
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.
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
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."
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
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
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.
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
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
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.
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."
SiteGround - Reliable hosting with speed, security, and support you can count on.
lalitkale
syuchimu
kumorn5s
uvb_76
starfish719
gawa
dynamis
minorun365
muehara
visional_engineering_and_design
amacbee
tagomoris
rfdnxbro
aki_iinuma
62gerente
matthewcrist
mayunak
pauljervisheath
bethany
kevinliebholz
tammyeverts
bkeepers
addyosmani
vanstee
frankvandijk
