Agentic Engineering Course

What you'll learn:

using artificial intelligence. You'll learn how to use AI not just for code generation, but as a full-fledged engineering tool for creating high-quality, scalable, and maintainable applications. Instead of chaotic vibe coding and endless prompts, the course offers a systematic approach: context management, architecture planning, quality control, and verification of AI results.

What is the course about?

The training is based on JSM Skills Workflow , a practical AI development system created through 12 months of experimentation with artificial intelligence tools. The methodology combines the automation of routine processes with engineering control, helping to effectively work on projects of varying complexity.

The course demonstrates how to transform AI from a random assistant into a controlled participant in the development process: from task definition and context preparation to implementation, review, and release of finished functionality.

What you will learn

• Manage the AI context. Determine what information to pass to the model and what to exclude to obtain accurate results without overloading the context window.

• Monitor the quality of code generation. Identify errors, inconsistencies, and deviations from the original plan early in development.

• Correct AI performance. Return the model to the correct solution without having to start the process over again.

• Plan the application architecture. Create a stable project structure that maintains integrity as new features are added.

• Work with the JSM Skills Workflow. Use ready-made commands and tools to automate the full development cycle of individual features.

• Automate development. Transform a brief task description into a ready-made implementation, taking into account the existing codebase and project requirements.

• Review code using multiple AI models. Organize independent reviews using a second model to improve the quality and reliability of solutions.

• Make engineering decisions. Understand when AI recommendations can be trusted, when intervention is necessary, and which decisions should not be delegated to the model.

• Optimize costs. Reduce the costs of developing, maintaining, and scaling applications through a well-designed AI workflow.

How JSM Skills Workflow Works

The development process is organized as a repeatable cycle for each new feature. This approach makes working with artificial intelligence more predictable and helps maintain control over the architecture, code quality, and deadlines.

Main stages of workflow

1. Problem statement. You describe the required functionality with a single command, for example /develop build the checkout page.

2. Context preparation. The system analyzes the existing code and project plan, then generates detailed instructions for the AI.

3. Coordination of solutions. You review the proposed approach, make adjustments, and confirm further work.

4. Implementation. AI writes code taking into account the architecture, conventions, and existing project templates.

5. Independent verification. Another AI model analyzes the results and helps identify potential issues.

6. Task completion. Once verified, the functionality is ready for release, and the process can be repeated for the next task.

Practical focus of the course

Particular attention is paid to situations where the AI begins to deviate from requirements, makes unfounded architectural decisions, or creates code that works now but causes problems as the project progresses.

Using practical examples, the author demonstrates the differences between standard vibe coding and system-level AI development. He also compares the costs of creating, operating, and scaling the same application using different approaches to AI.

Who is this course suitable for?

This course is designed for developers who already know how to program and use artificial intelligence in their daily work, but want to make this process more predictable, efficient, and professional.

• For frontend, backend, and full-stack developers who want to integrate AI into their workflow.

• For engineers tired of chaotic vibe coding and unstable code generation results.

• For developers who want to better manage the architecture and quality of projects created with AI.

• Team leads and technical specialists who need to understand how to safely use AI in team development.

Knowledge of TypeScript or experience as a Senior Developer is not required, but basic programming skills are a must.

What will you get as a result?

After completing this course, you'll be able to not only generate working code from a neural network, but also manage the entire engineering development process with AI: from problem setting and architecture selection to quality assurance and final product release.

The main goal of this course is to teach you how to use agent engineering as a professional approach to software development, where artificial intelligence accelerates work, but key engineering decisions remain under your control.