Model Online / On-Site
Duration
Modules 6 modules

Course Objective & Scope

Treating AI coding tools merely as autocomplete speeds up typing but often inflates pull-request queues and architectural drift when team standards lag behind. This multi-module academy reshapes the entire software development lifecycle (SDLC)—from specification and architecture to multi-file agentic coding, legacy refactoring, test automation, code review and DevSecOps—into a disciplined AI-native workflow.

Across the academy, engineering teams configure repository-level context (AGENTS.md, rule files, MCP integrations), practise spec-driven development and establish human verification gates, leaving with a validated codebase deliverable and a team-wide AI engineering playbook.

Who Should Attend?

  • Frontend, backend, full-stack and mobile software engineers
  • Software architects, staff/principal engineers, tech leads and engineering managers
  • QA automation, DevOps/platform and application security (AppSec) engineers
  • Prerequisite: Active software development experience and familiarity with Git workflows

Key Takeaways

  • Structure repository context rules, architectural constraints and MCP tools so coding agents follow team conventions
  • Run spec-driven, multi-file agentic coding loops rather than undisciplined 'vibe coding'
  • Understand and refactor complex legacy modules safely using characterisation tests and incremental migration
  • Generate high-value unit, integration, contract and end-to-end tests covering edge cases and failure modes
  • Enforce PR review, AppSec scanning and DORA/SPACE metrics tailored to AI-assisted software delivery

Curriculum

From Autocomplete to AI-Native SDLC: Spec-Driven Development
  • Choosing between inline completion, chat, IDE agents and CLI/background agents
  • Co-authoring technical specs (PRDs, RFCs, ADRs) and task breakdowns before generating code
Repository Context Engineering, Rule Files and MCP
  • Codebase indexing, AGENTS.md / IDE rule files and enforcing architectural boundaries
  • Connecting coding agents to schemas, API specs, issue trackers and logs via MCP
Multi-File Implementation, Legacy Comprehension and Safe Refactoring
  • Impact analysis, dependency tracing and root-cause debugging in unfamiliar codebases
  • Writing characterisation tests first and executing incremental legacy migrations
AI-Accelerated Test Engineering and Quality Assurance
  • AI-assisted TDD, edge-case generation, property testing and realistic fixtures/mocks
  • Automating API contract tests and maintaining resilient end-to-end test suites
DevSecOps Guardrails and AI-Era Pull Request Discipline
  • Catching AI-induced vulnerabilities, secrets exposure and hallucinated packages (slopsquatting)
  • Keeping PRs reviewable, combining automated pre-reviews with rigorous human sign-off
Engineering Metrics, Team Playbook and Capstone Delivery
  • Measuring real impact beyond acceptance rate using cycle time, change failure rate and code churn
  • Delivering a validated feature or refactoring on an internal case and finalising the team playbook

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