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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 phases — a spec'd prototype in Orbit 1, a hardened production system in Orbit 2, and a scalable multi-agent platform in Orbit 3.

12 phases · 62 lessons · 62 labs · 3 projects

Take ML & AI Engineering first — this course builds on it.

Outcomes you will have by the end

• A production platform with full documentation — One real product, built across all 12 phases, with a visible spec directory, harness config, and quality-gate history — not just working code.
• 3 projects across the full arc — A spec'd prototype (Orbit 1), a hardened production system with observability (Orbit 2), and a scalable multi-agent platform (Orbit 3) — one product at three maturity levels.
• Verified certificate of completion — Issued by DeVenture Academy, tied to your completion record. Lists the specific spec, harness, and platform skills you completed.
• A production readiness review and remediation plan — A scored readiness checklist across 7 categories — spec coverage, test coverage, quality gates, observability, incident response, harness regression, platform architecture — with gaps triaged by real risk.
• Interview-ready career package — A portfolio case study, a mock interview presentation, a resume tailored honestly to real postings, and a career roadmap targeting a specific company archetype.
• 62 labs with real working artifacts — Every lesson ships a genuinely useful code artifact — a drift detector, a harness ownership router, a quality gate, an incident classifier — not a toy exercise.

What you will be able to do

Spec-Driven Development · Context Engineering · Harness Engineering · Agent Orchestration · Quality Gates & CI/CD · Production Observability · Team Workflows

Every phase, every lesson, every project

• The AI-Native Engineering Mindset (5 lessons) — free — Implementer vs. orchestrator, the specify→generate→validate→ship→operate loop, the tool landscape by category, model selection as an engineering decision, the sycophant problem
  • From Implementer to Orchestrator
  • The AI-Native Engineering Loop
  • The Tool Landscape: Durable Categories, Not Brands
  • Model Selection as an Engineering Decision
  • The Sycophant Problem and the Struggle Principle
• Spec-Driven Development (5 lessons) — free — 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
  • Why Specs Before Code
  • Writing Your First Spec
  • Spec-Kit and the SDD Toolchain
  • Spec-Code Drift and How to Prevent It
  • From Spec to Plan to Tasks
• Context Engineering for Builders (5 lessons) — The context window as RAM, the write/select/compress/isolate framework, context failure modes (poisoning, distraction, confusion, clash), rules/skills/MCP as persistent context
  • What Context Engineering Actually Is
  • The Four Strategies: Write, Select, Compress, Isolate
  • Context Failure Modes
  • Rules, Skills, and Persistent Context
  • Building a Context Architecture for Your Product
• The Generate-Validate Loop (6 lessons) — Steering agents from a spec, validating beyond "it works," test-driven AI generation, debugging AI-generated code, git as a safety net
  • The Generate Phase: Steering Agents Effectively
  • The Validate Phase: Beyond "It Works"
  • Test-Driven AI Generation
  • Debugging AI-Generated Code
  • Git as a Safety Net for AI Code
  • Project: Spec'd & Validated Prototype
• Harness Engineering (5 lessons) — 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 = Model + Harness
  • Building a Harness for Your Product
  • Feedback Loops and Self-Correction
  • Approval Gates and Blast Radius Limits
  • Harness Regression Testing
• Agent Orchestration Patterns (6 lessons) — Workflows vs. agents, the five workflow patterns, single-agent architecture done right, multi-agent orchestration and anti-patterns, MCP and tool integration
  • Workflows vs. Agents: When to Use Which
  • The Five Workflow Patterns
  • Single-Agent Architecture Done Right
  • Multi-Agent Orchestration
  • MCP and Tool Integration Architecture
  • Orchestration for Your Product
• Quality Gates & CI/CD for AI Code (5 lessons) — 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
  • The Pre-PR Pipeline
  • Intent Verification
  • Spec-Code Drift Gates in CI
  • Multi-Agent Test Generation in CI
  • Building the Quality Gate for Your Product
• Production Observability & Incident Response (6 lessons) — AI-native observability signals, quality monitoring and rolling-mean drift detection, the four incident classes, the incident response playbook, the fail-plausible problem
  • AI-Native Observability
  • Quality Monitoring at Scale
  • The Four Incident Classes
  • Incident Response Playbook
  • The Fail-Plausible Problem
  • Project: Production-Grade System
• Multi-Agent Systems & Platform Architecture (5 lessons) — The layered AI-native platform architecture, multi-agent production patterns, agent memory architecture, long-running agent systems, scaling a product to a platform
  • Platform Architecture for AI-Native Products
  • Multi-Agent Production Patterns
  • Agent Memory Architecture
  • Long-Running Agent Systems
  • Scaling Your Product to a Platform
• Team Workflows & AI-Native Process (5 lessons) — 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
  • The AI-Native Team Operating Model
  • Spec Governance Across Teams
  • AI Code Review at Scale
  • Knowledge Management for AI-Native Teams
  • Process Design for Your Product
• Career, Portfolio & Interview Engineering (5 lessons) — 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
  • The AI-Native Product Engineer Role
  • Building the Portfolio
  • Interview Engineering: System Design for AI-Native Products
  • Interview Engineering: Live AI-Native Coding
  • Resume, LinkedIn & Job Search Strategy
• Capstone: The AI-Native Product Engineer (4 lessons) — Reviewing the full 3-orbit arc, the production readiness review, the demo day presentation, the final platform and career portfolio
  • Capstone Assembly: Reviewing the Full Arc
  • Capstone Hardening: The Production Readiness Review
  • Capstone Presentation: The Demo Day
  • Project: Full Platform & Career Portfolio

The technologies you will use

Claude Code · GitHub Spec Kit · Python · GitHub Actions · CodeRabbit · MCP · Langfuse · Docker

Roles this course prepares you for

• AI-Native Product Engineer ($150k–$260k) — Own the full spec-to-production loop for AI-native features: writing specs, engineering harnesses and quality gates, orchestrating agents, and operating the system in production. The role this course is built around.
• Applied AI Engineer ($155k–$210k) — Build production AI features at companies retrofitting AI onto existing products or building AI-native from day one — the most common title hiring for this exact skill set in 2026.
• AI Platform Engineer ($135k–$195k) — Build the internal platform other engineers use to ship AI-native features: multi-agent orchestration, memory systems, team workflows, and the quality gates that keep AI-generated code safe to merge.
• Frontier AI Engineer ($250k–$600k+ total comp) — Research-adjacent engineering at a frontier lab, at a comp tier and scale categorically different from the other three archetypes — this course builds the engineering discipline, but this specific archetype typically also requires deeper ML/systems background than this course alone provides.

What AI-native product engineering actually is

AI-native product engineering is the discipline of building products where AI is a first-class citizen in the development process — not a bolted-on feature. You write specs that AI agents can execute, engineer context windows for maximum signal, build harnesses that constrain AI output, and orchestrate agents that ship production code. It is not "vibe coding" — it is the engineering discipline that makes AI-assisted development reliable, reviewable, and production-ready.

What you do every day

You write machine-readable specs, engineer context files that give AI agents the right information, build harnesses that enforce quality gates on AI-generated code, and orchestrate multi-agent workflows for complex features. You review AI output, verify correctness, and ship with confidence because the process is structured — not improvised.

Why companies hire for this

Every company wants to use AI to ship faster. Most cannot because their process is unstructured — prompts are ad-hoc, context is missing, and AI output is unreviewed. Companies need engineers who can build the spec-driven, context-engineered, quality-gated workflow that makes AI-assisted development trustworthy.

What this course is not

It is not a "use Claude Code" tutorial. You will not just type prompts and hope for the best. You will build the engineering stack — specs, context, harnesses, agents, quality gates — that makes AI-native development a discipline. The process is what makes this course different from every "AI coding" course.

Common questions

When does AI-Native Product Engineering launch?

The 12-phase curriculum is still in final review and is not included in the live course count. We will mark it available only after its lessons, labs, and end-to-end project flow pass the same release checks as the current catalog.

What background do I need?

Comfortable with Python — functions, classes, basic async is helpful but not required. No prior agent, RAG, or ML experience needed; Phase 0 introduces the tool landscape and model selection from scratch.

How is this different from Vibe Coding, Testing AI-Written Code, and AI Product Management?

Vibe Coding teaches the "describe and ship" pattern for non-technical builders and stops at prototypes — this course is the production-focused evolution of it, requires code, and goes all the way to a deployed, observable platform. Testing AI-Written Code focuses on verification specifically; this course covers the full engineering lifecycle, of which testing is one phase. AI Product Management covers product strategy; this course covers the engineering practice of actually building with AI.

What is the "3-orbit spiral" structure?

Instead of flat phases, this course revisits the same specify→generate→validate→ship loop three times at increasing depth. Orbit 1 (phases 0–3) produces a spec'd prototype. Orbit 2 (phases 4–7) hardens it into a production system with quality gates and observability. Orbit 3 (phases 8–11) scales it into a multi-agent platform with team workflows and a career-ready portfolio. One product, built across all three.

Do I need to already have a product idea?

No — Phase 0 introduces the course-long product concept and helps you pick one. You build the same real product across all 12 phases, so it becomes a genuine portfolio centerpiece by the end, not 12 disconnected exercises.

What jobs does this course prepare me for?

AI-Native Product Engineer, Applied AI Engineer, and AI Platform Engineer, with real 2026 comp data covered in Phase 10 — plus an honest look at what a frontier-lab track would additionally require.

Key terms in this course

Agent · Orchestration · MCP (Model Context Protocol) · Context Window · Regression Testing

Continue your learning path

Agentic AI Engineering · Testing AI-Written Code · Vibe Coding

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