Tiered Context Loading Solves Agent Registry Overflow in AI Dispatcher Systems

A software engineer discovered that their multi-agent dispatcher system was architecturally unsustainable after doing the math on context window limits. With a realistic registry of 200–400 capabilities, each spec consuming 4–8 kilobytes, the total data required could reach 3.2 million tokens — roughly 25 times the 128K-token limit of GPT-4o. The system had only appeared functional because the live registry remained small enough to avoid hitting that ceiling. To address this, the engineer explored tiered context loading, a pattern sourced from the open-source KARIMO project, which avoids loading all capability specs upfront. Simple on-demand fetching was found to be inadequate on its own, as blind routing or brittle keyword matching introduced new failure modes in production environments.