context partitioning

Separating global task objectives, agent-specific instructions, and shared knowledge is fundamentally context partitioning across agent boundaries

Task decomposition into specialized agents inherently requires context boundaries—each agent focuses on relevant information for its role. The use case diversity (financial analysis, research, creativ

Memory taxonomy (short/long/shared) and per-agent tool assignment implies partitioning context by agent domain

The distinction between 'each agent has its own prompt, model, tools' vs. shared state is a context partitioning decision—what's isolated vs. what's shared.

Each agent receives constrained context slice based on its role; partitioning reduces cognitive load per agent

The 'balancing act' comment suggests each agent receives different context based on role; this is implicit context partitioning strategy

Multi-agent design implicitly partitions context across agents (each agent gets specialized context for their role), though article doesn't explicitly frame it this way.