Context Engineering is where AI Agents succeed or fail

When an AI Agent fails in production, the model is usually not the problem. The information it received at that moment is. Most of the work of building reliable agents comes down to context engineering, namely what reaches the model, when it reaches it, and how it is presented.

When an AI Agent makes a bad decision thirty steps into a workflow, the useful question is not whether the model is unreliable. It is what information was in the context window at that point, and why that information made the wrong answer more likely than the right one. That question is usually answerable, and usually fixable.