AI-Native Product Engineering
From vibe coding to production — the full engineering discipline behind AI-native software
12 phases across 3 orbits. 62 lessons. 62 labs. 3 projects. The engineering discipline behind building with AI as a structured stack, not a chatbot: spec-driven development, context engineering, the generate-validate loop, harness engineering, agent orchestration, quality gates and CI/CD for AI code, production observability and incident response, multi-agent platform architecture, team workflows, and career positioning as an AI-native product engineer. One real product, built across all 12 phas
- Lessons: —
- Labs: —
- Projects: —
- Level: Beginner
Curriculum
- The AI-Native Engineering Mindset — Implementer vs. orchestrator, the specify→generate→validate→ship→operate loop, the tool landscape by category, model selection as an engineering decision, the sycophant problem
- Spec-Driven Development — Why specs before code, spec structure and the specificity bar, GitHub Spec Kit and the SDD toolchain, spec-code drift and the drift gate, decomposing a spec into a plan and tasks
- Context Engineering for Builders — The context window as RAM, the write/select/compress/isolate framework, context failure modes (poisoning, distraction, confusion, clash), rules/skills/MCP as persistent context
- The Generate-Validate Loop — Steering agents from a spec, validating beyond "it works," test-driven AI generation, debugging AI-generated code, git as a safety net
- Harness Engineering — Agent = model + harness, building a harness for a real product, feedback loops and self-correction, approval gates and blast radius limits, harness regression testing
- Agent Orchestration Patterns — Workflows vs. agents, the five workflow patterns, single-agent architecture done right, multi-agent orchestration and anti-patterns, MCP and tool integration
- Quality Gates & CI/CD for AI Code — The pre-PR pipeline (lint + SAST + dependency scan + AI review), intent verification, spec-code drift gates in CI, multi-agent test generation, building the full quality gate
- Production Observability & Incident Response — AI-native observability signals, quality monitoring and rolling-mean drift detection, the four incident classes, the incident response playbook, the fail-plausible problem
- Multi-Agent Systems & Platform Architecture — The layered AI-native platform architecture, multi-agent production patterns, agent memory architecture, long-running agent systems, scaling a product to a platform
- Team Workflows & AI-Native Process — The AI-native team operating model, spec governance across teams, AI code review at scale, knowledge management for AI-native teams, process design for a real product
- Career, Portfolio & Interview Engineering — The AI-native product engineer role and company archetypes, building the portfolio, system design interviews, live AI-native coding interviews, resume/LinkedIn/job search strategy
- Capstone: The AI-Native Product Engineer — Reviewing the full 3-orbit arc, the production readiness review, the demo day presentation, the final platform and career portfolio
Skills You Will Learn
- Spec-Driven Development
- Context Engineering
- Harness Engineering
- Agent Orchestration
- Quality Gates & CI/CD
- Production Observability
- Team Workflows
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