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Part 4: Coding for Problem Solving

In Parts 1-3, you discovered the AI development revolution, mastered AI collaboration tools, applied General Agents to real workflows, and understood specification-driven development. Now you're ready to formalize the axioms of agentic development and learn the programming language that powers AI agents: Python.

This part begins with Chapter 14: Ten Axioms of Agentic Development, which formalizes the principles you've experienced implicitly into a rigorous framework. Then across 18 chapters (Chapters 15-32), you'll master Python—from package management to asynchronous programming, from object-oriented design to production-ready systems—all while maintaining the AI-first development approach you've internalized.

Python is the lingua franca of artificial intelligence. When you build agentic systems, train models, or integrate AI APIs, you'll almost certainly be working in Python. But this isn't a traditional programming tutorial where you memorize syntax and copy code from textbooks. You'll learn Python the AI-native way: by understanding concepts deeply and expressing your intent to AI collaborators who handle the mechanical details.

The journey begins with tooling (UV package manager) before diving into language fundamentals. This isn't accidental: modern Python development requires professional-grade project management from day one. You'll set up production-quality environments before writing your first print() statement.

What You'll Learn​

By the end of Part 4, you'll understand:

  • Modern Python tooling: Professional project setup with UV (the fastest Python package manager), dependency management with lockfiles, virtual environment isolation, and reproducible team workflows—configured through AI collaboration rather than memorizing CLI flags
  • Python fundamentals: Data types, operators, control flow, and functions with type hints—learning syntax through practical examples that AI helps you write, not rote memorization exercises
  • Data structures and collections: Lists, tuples, dictionaries, sets, and their performance characteristics—understanding when to use each structure through AI-guided architectural decisions
  • Object-oriented Python: Classes, inheritance, composition, magic methods, metaclasses, and dataclasses—designing systems that AI can extend while maintaining clean architecture
  • Modern Python patterns: Pydantic for validation, generics for type safety, asyncio for concurrent operations—leveraging AI to implement patterns correctly the first time
  • Production readiness: Exception handling, file I/O, testing strategies, documentation with MkDocs—building software that survives contact with real users and real infrastructure
  • Python internals: CPython implementation details, the Global Interpreter Lock (GIL), memory management—understanding what happens beneath the surface when AI generates code
  • Your Python identity: Positioning yourself as an architect who understands Python's strengths and limitations, knows when to apply advanced features, and collaborates with AI to build maintainable, scalable systems

What's Next​

After completing Part 4, continue to Part 5: Building Custom Agents where you'll apply your Python skills and axiom-grounded thinking to build production AI agents with SDKs like OpenAI Agents, Google ADK, and Claude Agent SDK.

The Python skills you build here become the foundation for every technical part that follows—agentic AI development, MCP servers, TypeScript integration, real-time systems, and production deployment. You're not just learning a language. You're gaining the technical fluency to orchestrate AI systems professionally.