There's a better way. Agentic AI Programming for Python teaches you to work with AI that acts like a skilled junior developer on your team
Have you tried AI coding tools only to end up frustrated, copying and pasting broken code from chatbots, or cleaning up poorly structured "AI slop"? There's a better way. Agentic AI Programming for Python teaches you to work with AI that acts like a skilled junior developer on your team: One who understands your entire codebase, runs your tests, formats your code, and builds complete features autonomously while you provide guidance and direction. Michael Kennedy shows you how to leverage tools like Cursor and Claude to build production applications, from greenfield projects to enhancing legacy systems, with well-structured code, comprehensive testing, and proper error handling. You'll learn the crucial difference between chat-based AI and agentic tool-using AI that can read documentation, execute commands, and self-correct when things go wrong. Through hands-on examples including a complete CLI tool, web applications, and real features running in production today, you'll master the guardrails, roadmaps, and workflows that turn AI from a frustrating experiment into a genuine force multiplier for your development work.
What's this course about and how is it different?
This course teaches you how to collaborate with agentic AI tools, not just chatbots or autocomplete, but AI that can understand your entire project, execute commands, run tests, format code, and build complete features autonomously. You'll learn to guide these tools like you would a talented junior developer on your team, setting up the right guardrails and roadmaps so they consistently deliver well-structured, maintainable code that matches your standards. Think of it as pair programming with an AI partner who learns your preferences, follows your conventions, and gets more effective the better you communicate.
Why it stands out
Focus on agentic AI, not chatbots – Learn the fundamental difference between copying code from ChatGPT and collaborating with AI that operates within your codebase with full context
Real production examples – See actual features Michael built for Talk Python and Python Bytes that are running in production today
Teaching AI your standards – Discover how to configure AI to write well-factored, properly structured code with type hints, error handling, and comprehensive tests -- not generic "AI slop"
Comprehensive guardrails and roadmaps – Master Cursor rules, slash commands, agents, and documentation integration that guide your AI partner to work exactly how you want
Both greenfield and legacy projects – Learn strategies for starting from scratch and enhancing existing codebases with AI assistance
Visual design with screenshots – See how to use images to communicate design intent and iterate on UI/UX with remarkable precision
Version control as a safety net – Understand the critical role of Git workflows when working with AI to fearlessly experiment and roll back when needed
Practical planning techniques – Learn when to plan first, how to break work into phases, and when to use structured roadmaps versus direct implementation
Cost and model selection guidance – Get specific recommendations on which models to use, when to upgrade or downgrade, and how to monitor your AI usage
Real-world productivity gains – See how features that would take days or weeks can be completed in hours with proper AI collaboration
What topics are covered
By the end of this course, you'll be able to
Distinguish between agentic AI, chat-based AI, and autocomplete tools and choose the right approach for each situation
Set up Cursor with Claude models (or alternatives like Claude Code, Cline, GitHub Copilot) for maximum effectiveness
Configure cursor rules at both machine-wide and project-specific levels to teach AI your coding standards and preferences
Create detailed implementation plans that your AI partner can follow step-by-step to build complex features
Use source control strategically with frequent commits, staging, and rollbacks to work fearlessly with AI
Build complete Python CLI applications from scratch with proper package structure, dependencies, and distribution setup
Enhance existing production web applications with new features while maintaining consistency with legacy code
Integrate custom documentation for lesser-known Python packages so AI can use them effectively
Create reusable slash commands for repetitive review and quality assurance tasks
Define custom agent personas (like "Brand Guardian" or "Test Reviewer") to get specialized perspectives
Use screenshots and visual examples to communicate design intent and iterate on web interfaces
Manage AI context windows effectively to keep conversations focused and costs under control
Choose the right AI model for planning versus implementation based on complexity and budget
Monitor and predict your AI usage to avoid running out of credits mid-project
Apply "product manager" thinking to get detailed specifications and architecture plans from AI
Structure multi-phase projects with clear milestones and deliverables
Guide AI to self-correct when it encounters errors, missing dependencies, or type checking issues
Ensure AI-generated code includes proper error handling, logging, and deployment-ready features
Use parallel processing, async/await, and modern Python patterns in AI-generated code
Create comprehensive test suites with proper coverage and configuration
Handle both new projects and legacy codebases effectively with AI assistance
Who Should Take This Course?
This course is perfect for
Python developers who have tried AI coding assistants but found them frustrating, inconsistent, or producing low-quality code
Professional software engineers who want to dramatically increase productivity without sacrificing code quality or maintainability
Technical leads and architects who need to evaluate and adopt AI coding tools for their teams with confidence
Solo developers and indie hackers who want to build features and utilities that were previously too time-consuming to justify
Anyone maintaining legacy codebases who needs to add features to older projects without creating more technical debt
Developers skeptical of AI coding tools who want to see what's actually possible with proper setup and clear communication

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