context-engineering

Manage context in multi-agent systems by enforcing progressive disclosure and context budgeting to load only necessary data at request time. This prevents cross-task contamination through sub-agent isolation, sustaining agent reliability while reducing token usage and latency as conversations grow.

Context budgeting optimizes prompt management by designing prompts and tool data to respect attention budgets and minimize quality loss. It reduces overall context size while preserving signal, ensuring agents process only essential information without degraded performance as conversations grow.

Reduce token usage and latency by applying progressive disclosure to load only necessary parts of retrieved documents at request time. Map current prompt pipelines to identify system prompts and tools, then introduce just-in-time loading rules to minimize context size while preserving signal.

Agents forget critical instructions as message history grows because bulky context degrades attention and recall. Place essential instructions at the edges of the context window for robust recall, and apply context isolation to partition tasks, preventing critical information from being lost mid-context.

Isolate sub-agents in multi-agent pipelines when you need to prevent cross-task contamination and sustain reliability. Partitioning tasks into isolated contexts ensures tool outputs, message history, and retrieved documents from one task do not degrade another task's processing performance.