foreword

Ask a room full of software engineers whether they are using AI, and you’ll likely get a very confident yes. AI usage is high, and companies have the token bills to prove it. And yet, in many organizations, the actual impact is harder to find.

That was the uncomfortable gap I ran into while transforming a 3,500 person engineering organization to integrate AI agents into their workflows. On paper, adoption was strong, with 95% of engineers using coding agents every day. In practice, features were not reaching customers any faster. Engineers were using AI, but it still was not part of how we built and shipped software.

That gap is what makes context engineering so important.

For a while, much of the industry treated this as a prompt engineering problem. If the model gave a poor answer, we tweaked the prompt and hoped for a better result. But that doesn’t scale to production systems, especially large, messy codebases with established team conventions.

In Context Engineering, Boni García gives a name and structure to something many of us have been learning the hard way. He does not reduce context to a bigger prompt or a longer context window. He treats it as the full operating environment around the model, including the information, tools, and boundaries that shape its behavior.

That shifts the conversation from "How do I write a better prompt?" to "What does this system need to behave reliably?" That’s a much more useful question.