Model Online / On-Site
Duration
Modules 5 modules

Course Objective & Scope

Most organisations bolt AI onto the coding step alone, only to discover that doubling code generation speed clogs pull-request queues, overwhelms QA and increases production incidents. This course teaches engineering leaders and platform teams how to redesign the entire Software Development Life Cycle (SDLC) so speed, quality and security scale together.

Upstream specification quality, repo-level context standards, small-PR discipline, automated verification gates, risk-tiered human review, CI/CD and QA integration, incident feedback loops and measuring real impact with DORA/SPACE metrics are worked through. Participants leave with a phased AI-Native SDLC blueprint tailored to their organisation.

Who Should Attend?

  • VPs of Engineering, engineering directors and software development managers
  • Principal/staff architects and Platform/DevEx team leads
  • Quality (QA), DevSecOps and delivery transformation leaders
  • Prerequisite: experience managing or architecting enterprise software delivery lifecycles

Key Takeaways

  • Diagnose how AI shifts SDLC bottlenecks from typing code to specification, review and verification
  • Define human-vs-agent responsibilities and deterministic quality gates at every SDLC phase
  • Prevent PR queue gridlock through vertical slicing, automated pre-review and risk-tiered sign-off
  • Measure real engineering ROI using DORA, PR cycle time and change-failure rate instead of vanity metrics
  • Build a 90-day rollout plan from pilot squads to organisation-wide engineering standards

Curriculum

Post-AI SDLC Bottlenecks and Systems Thinking
  • What happens to PR review, testing and integration queues when code output doubles
  • Shifting from typing-centric workflows to Spec-First, Verification-Heavy delivery
Shift-Left: Requirements, Architecture and Repo Readiness
  • Eliminating ambiguous tickets, contract-first design and ADR standards
  • Making repositories agent-ready with AGENTS.md, fast test harnesses and strict typing
The Core Loop: Coding, PR Sizing and Layered Review
  • Enforcing small vertical or stacked PRs to prevent unreviewable AI code dumps
  • Combining deterministic CI gates, automated AI pre-review and human architectural sign-off
Shift-Right: QA, Security, Release and Incident Feedback
  • Continuous test generation, SAST/SCA gates and feature-flagged progressive delivery
  • Closing the loop from production post-mortems back into repo rules and regression suites
Engineering Metrics (DORA / SPACE) and Rollout Blueprint
  • Replacing vanity autocomplete metrics with lead time, code churn and change-failure rate
  • Protecting junior-engineer learning paths and executing a phased 90-day rollout

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