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Part 6: SDD-RI: AI Products Engineering

You've mastered SDD-RI fundamentals (Part 4) and built Python expertise (Part 5). Now you're ready to think beyond code execution—to product thinking, business strategy, agent team management, and the leadership mindset that separates builders from architects.

Part 6 bridges technical capability to strategic leadership. You'll learn evaluation-first development, agent team orchestration, and the decision frameworks that enable solo developers to build products previously requiring 20+ person teams.

Goals​

By completing Part 6, you will:

  • Master evaluation-first development: Learn to build validation frameworks BEFORE features, ensuring AI systems meet user needs across diverse scenarios
  • Orchestrate agent teams: Manage multiple AI agents working together—coordinating autonomous capabilities into cohesive products
  • Lead AI product development: Make strategic architecture decisions, coordinate technical teams, and scale products from prototypes to production systems
  • Think strategically: Move from "can I build this?" to "should I build this?"—validating product-market fit before scaling development
  • Build sustainable systems: Balance technical debt, iteration speed, and long-term maintainability through principled decision-making

Why This Part Matters​

Traditional software engineering teaches you to write code. AI-native development teaches you to orchestrate systems.

In 2025, the constraint isn't coding ability—AI agents handle implementation. The constraint is strategic thinking: knowing what to build, how to validate it works, and how to coordinate multiple capabilities into products users trust.

This part teaches you to think like a product leader:

  • Before building: Design evaluation frameworks that prove your feature works
  • During building: Orchestrate multiple AI agents working in parallel
  • After building: Make architecture decisions that let systems scale from 100 to 100,000 users

You're not just learning how to prompt AI. You're learning how to lead AI systems at scale.

Chapter Progression​

Part 6's 3 chapters build leadership capability through three complementary perspectives:

Chapter 34: AI Product Development & Evaluation-First Mindset​

Start with the foundational mindset shift: evaluations before features.

You'll learn:

  • Why building evals first prevents wasted development effort
  • How to design product metrics that matter (user satisfaction, accuracy, safety, cost)
  • How to implement validation frameworks that test AI systems against real-world scenarios
  • How to apply business thinking—connecting technical capabilities to market needs and competitive positioning

Why first? You can't manage what you can't measure. Evaluation-first thinking establishes the validation foundation that makes agent orchestration (Chapter 35) and leadership decisions (Chapter 36) possible.

Chapter 35: AI Orchestra: Managing Agent Teams​

Build on evaluation frameworks by learning to coordinate multiple AI agents working together.

You'll learn:

  • How to design agent architectures—breaking complex products into specialized capabilities
  • How to implement coordination patterns—routing, delegation, and consensus protocols
  • How to manage agent state—memory, context sharing, and distributed coordination
  • How to debug multi-agent systems—observability patterns for complex interactions

Why second? Once you can validate individual capabilities (Chapter 34), you're ready to compose them into systems. Agent orchestration is the core skill that enables building products 10x faster than traditional development.

Chapter 36: Product & Engineering Leadership for AI Systems​

Apply evaluation-first thinking and agent coordination skills to strategic leadership.

You'll learn:

  • How to make architecture decisions that scale—infrastructure, deployment, observability
  • How to coordinate technical teams (human + AI)—spec-driven workflows and async collaboration
  • How to manage technical debt strategically—when to move fast vs. build foundations
  • How to lead through uncertainty—making decisions with incomplete information
  • How to communicate technical complexity to non-technical stakeholders

Why third? Leadership requires composing knowledge from Chapters 34-35. You'll apply evaluation frameworks to architecture decisions and agent coordination patterns to team management—synthesizing technical and strategic thinking.

How These Chapters Connect​

Part 6 builds through progressive integration:

Chapter 34 (Evaluation-First) establishes the validation mindset:

  • You learn to measure success before building
  • You design metrics that guide development decisions
  • You implement evaluation frameworks that prove systems work

Chapter 35 (Agent Orchestration) applies evaluation to multi-agent systems:

  • You coordinate multiple capabilities validated by Chapter 34's frameworks
  • You debug complex interactions using evaluation-based observability
  • You build products by composing specialized agents

Chapter 36 (Leadership) synthesizes evaluation + orchestration into strategic thinking:

  • You make architecture decisions validated by evaluation frameworks
  • You coordinate teams using agent orchestration patterns
  • You scale products by thinking in systems, not features

Methodology Note​

Part 6 uses all four teaching layers:

Layer 1 (Manual Foundation): Understanding evaluation design, agent coordination theory, and leadership frameworks Layer 2 (AI Collaboration): Working with AI to design evaluation datasets, implement orchestration patterns, and analyze strategic decisions Layer 3 (Reusable Intelligence): Creating evaluation templates, orchestration patterns, and decision frameworks that apply across projects Layer 4 (Spec-Driven Integration): Composing knowledge from Chapters 34-36 in capstone scenarios requiring strategic thinking

You'll experience the full progression—foundation building, AI collaboration, creating reusable components, and specification-first leadership—now applied to product thinking rather than just code execution.

Prerequisites​

Required:

  • Part 4 (SDD-RI Fundamentals): You'll apply specification thinking to product design—writing specs that include evaluation criteria and success metrics
  • Part 5 (Python Fundamentals): You'll implement evaluation frameworks, agent systems, and production patterns using Python

Helpful context (not required, but enriches learning):

  • Exposure to product development workflows
  • Experience with software architecture decisions
  • Understanding of business metrics (conversion, retention, cost)

Real-World Impact​

The skills in Part 6 transform how you build AI products:

Before Part 6:

  • Build features, hope they work
  • Debug by guessing
  • Scale by hiring more developers
  • Make architecture decisions by intuition

After Part 6:

  • Build evaluation frameworks, then features
  • Debug using systematic observability
  • Scale by orchestrating AI agents
  • Make architecture decisions using principled frameworks

This isn't incremental improvement—it's a paradigm shift in how you think about product development.

What Makes This Part Different​

Most AI education teaches tools (how to use Claude, GPT, Gemini). Few resources teach AI product leadership—the strategic thinking, coordination patterns, and decision frameworks that separate prototypes from production systems.

You'll develop capabilities that traditionally require 5-10 years of experience:

  • Designing systems that scale
  • Coordinating complex technical projects
  • Making strategic trade-offs between speed and quality
  • Communicating technical complexity to non-technical stakeholders

You're not just learning to prompt AI—you're learning to lead AI systems at scale.

Beyond Part 6​

After completing Part 6, you'll be ready for:

Part 7: AI Native Software Development — Apply product leadership principles to building production agent systems with modern frameworks (OpenAI Agents SDK, Google ADK, Anthropic Agents Kit), integration patterns (MCP), and data persistence (vector, relational, and graph databases)

Part 8: AI Cloud Native Development — Scale products from local development to production deployment—containerization, orchestration (Kubernetes, Dapr), and operational excellence (observability, security, cost optimization)

You're building the foundation for professional AI product development—the skills that enable solo developers to build systems that serve millions of users.