How to get started?

This “Getting Started” chapter is here to give you a concrete sense of what is possible with agentic engineering.

We cannot teach you every important technique right away. The detailed workflows, constraints, and tradeoffs come in the chapters that follow, but for now, the goal is simpler: to give you a small but useful amount of practical knowledge and to help you start working with agents in a real software project.

You need a real project

There is no efficient way to learn this subject purely in theory.

If you want to understand agentic engineering, you need to get your hands dirty in a living codebase. You need to see an agent misunderstand a task, surprise you with a good idea, take a wrong turn, recover, and help you ship something anyway. That feedback loop is where the real learning happens and appreciation of this technology can be born.

Working with LLMs is deeply individual. These systems are probabilistic. Human thinking is too. Pairing the two is never a perfectly standardized process. Each person develops their own way of briefing, steering, reviewing, correcting, and trusting an agent. Over time, everyone builds their own story of how they work with AI.

So yes, this chapter will show you tricks. But tricks are not enough. You need practice. Do not just read the prompts. Run them. Review the diffs. Follow the agent into mistakes. Ask it to recover. Start a new thread when the current one gets messy. Notice what kinds of instructions work well for you and which ones do not.

That is how you begin to build intuition. And once you have a bit of real experience, the rest of this book will become much more useful.

Two practical paths

Because hands-on work matters so much, this chapter is organized around two practical paths.

Path A: Start something new

The first path is to start a project from scratch with agentic engineering techniques from day one.

This path focuses on using agents early: to explore ideas, scaffold the project, shape the first implementation steps, and establish good habits before the codebase grows. The idea does not matter, and neither does your familiarity with the technology stack.

If you want to begin this way, continue with How to set up a new repo?.

Path B: Enter a mature project

The other path is to jump into an existing codebase and use agents to move faster and make better decisions there. If you already have access to a larger or longer-lived codebase, this might be a more productive choice.

This path is about using agents to understand unfamiliar systems and make surgical changes without losing control of the code. It does not matter whether that project is old or new. It does not matter whether it is tiny or huge. It does not matter whether you knew the project before you became interested in AI, or whether you want to learn the project and learn agentic engineering on top of it at the same time.

For this path, feel free to jump to First steps in mature projects.