Separate Context Windows
Separate context windows constitute an architectural pattern in multi-agent AI systems where each agent maintains its own isolated conversational and operational context. Rather than sharing a single context window across all agents in a system, this approach grants each agent independent memory of its interactions, instructions, and task progress. This isolation allows agents to operate with focused, relevant context without interference from the activities of other agents.
This pattern is particularly useful in complex agentic frameworks where distinct tasks require different sets of information or where the cumulative token usage of a shared context would exceed model limits. By partitioning context, systems can manage memory more efficiently, reducing latency and cost while preventing the degradation of performance that often occurs when irrelevant data crowds out critical instructions.
In implementations such as Claude Code sub-agents, separate context windows enable the main orchestrator to delegate specific sub-tasks to specialized agents. Each sub-agent processes its assigned portion of the workflow with its own dedicated context, ensuring that its reasoning remains precise and uncluttered by the broader system state. The results are then aggregated back to the primary agent, which maintains a high-level overview without being burdened by the granular details of every sub-task.
Source Notes
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