Token Economy
Token economy in the context of AI agents refers to the strategic management of computational resources and API costs when implementing multi-agent systems within Claude Code. As agent systems scale in complexity, the cumulative token usage across multiple sub-agents can quickly become prohibitively expensive if not carefully managed. Effective token economy requires deliberate architectural choices about how agents are decomposed, what information is passed between them, and how context is engineered.
Context Engineering
The primary lever for optimizing token economy is thoughtful context engineering. Each sub-agent incurs costs proportional to its input context size, so limiting what information each agent receives directly reduces expenses. This involves carefully selecting relevant system prompts, task descriptions, and reference materials for each agent while excluding unnecessary context. Reusing precomputed results across agents, rather than regenerating information, also reduces redundant token consumption.
Architectural Considerations
Token efficiency should influence how work is distributed across agents. Some tasks may be more economical to handle within a single agent rather than spawn multiple specialized agents with overlapping context. Similarly, the granularity of agent handoffs matters—frequent transfers of large documents between agents multiply costs, while consolidating related work reduces this overhead. Developers should regularly audit whether each agent justifies its existence through cost savings or performance gains that outweigh the context overhead it introduces.
Source Notes
- 2026-04-26: DeepSeek V4: China