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\u201cThe context window is the AI’s working memory for a session which has a limit. Depending on which underlying model is being used, performance can start to degrade before it even reaches its limit, so responses can become less accurate as a session goes on. GitHub Copilot helps with this by letting you compact a session, but if you trigger compaction after the underlying model has already started to degrade, the degraded state becomes your new baseline. That’s why I recommend structuring your workflow around this limit from the start.\u201d<\/p>\n
\u201cFor more complex features, I use the main chat thread to orchestrate that work. For a big feature, I\u2019ll have the main thread break the work into focused chunks, then start up background agents to handle each one. A coding agent handles the implementation, a validation agent reviews it, and once the review passes, the cycle repeats until the feature is done. Each agent works in its own context, so the main thread stays focused and performs consistently for longer.\u201d<\/p>\n<\/div>\n