customaize-agent:context-engineering

Context engineering identifies and optimizes context usage within agent systems to maximize relevance while minimizing token consumption across prompts, tools, and message history.

You manage context budgets by balancing token limits with necessary signals like system prompts, tools, and retrievals, applying progressive disclosure to load only needed context and prevent context degradation.

Progressive disclosure works by loading only the minimal, high-signal context window needed for a given task and dynamically loading additional materials on demand to keep responses fast and relevant.

Yes, you can use sub-agent isolation to structure tasks across lean contexts, which prevents context degradation and ensures reliable AI workflows when scaling multi-agent systems.

The best way to optimize token usage is applying explicit context budgets alongside compaction strategies and context tagging, measuring performance as context scales across multi-agent workflows.

You should apply context compaction strategies when scaling multi-agent workflows to ensure compatibility with sub-agent isolation and maintain performance while balancing token limits against essential signals.