context-engineering

Manage context limits for multi-agent language models by applying context budgeting and progressive disclosure. This technique loads only essential signals upfront and defers full content until a specific skill activates, maximizing decision quality under token constraints.

Progressive disclosure in agent prompt design is an on-demand content loading strategy. It defines guidelines for loading only task-specific context when needed, improving efficiency and safe prompt design by minimizing upfront token usage.

Isolate context for sub-agents in multi-agent orchestration by defining a context strategy that separates task-specific signals. This approach ensures scalable workflows by preventing context bleed and maintaining reliable decision quality across isolated agents.

The best way to improve context quality versus quantity in language-model agents is implementing just-in-time retrieval and verification. This ensures only essential, high-quality signals are loaded, mitigating degradation and maximizing decision reliability.

You need just-in-time content loading for agent memory management when working with scalable multi-agent workflows. It prevents context degradation by retrieving and verifying full content only when a task activates a specific skill requirement.

Context budgeting works for safe prompt design in multi-agent systems by limiting upfront signal exposure. It defines safe guidelines for just-in-time retrieval and progressive disclosure, ensuring reliable agent behavior under strict context limits.