context-engineering

Context compression reduces agent hallucinations by summarizing older conversation content, archiving low-signal items, and reclaiming context space after each turn, ensuring only high-relevance information occupies the model's context window.

The best way to enforce a token budget over large codebases is using a deterministic budget allocator that reserves headroom, allocates context window space by priority, and applies progressive disclosure to prevent truncation and overflow.

Progressive disclosure manages context windows by loading summaries first and escalating to deeper details only when needed, keeping token usage low while preserving access to critical information during agent execution.

Yes, context engineering is specifically designed for production deployments where token budgets are enforced, applying priority-based assembly and post-turn context reclamation to maintain agent quality without exceeding fixed token limits.

Agent response degrades during large domain knowledge injections because of context window bloat, which can be fixed by applying relevance scoring to order context by recency, importance, and task fit before assembly.

You should avoid context compression in agent workflows when maximum factual fidelity of raw conversation history is required, because summarization and archiving low-signal items inherently remove granular details to reclaim context space.