In AI products, context refers to the content, tools, and instructions provided to a model at any given moment. Because AI models have context limits, what's included (aka what a model is paying attention to) has a massive impact on results. So context management is key to letting people understand and shape what AI products produce. In Context Management UI in AI Products I looked at UI patterns for showing users what information is influencing AI model responses, from simple context chips to nested agent timelines. This time I want to highlight two examples of automatic and manual context management solutions. Augment Code's Context Engine demonstrates how automatic context management can dramatically improve AI product outcomes. Their system continuously indexes code commit history (understanding why changes were made), team coding patterns, documentation, and what developers on a team are actively working on.…