token-economy

Token optimization works by tracking token usage and dynamically matching task complexity with appropriate model selection and context management to minimize costs. It applies tier-based routing rules to classify work and assign suitable token budgets.

You reduce token costs by implementing task tier routing to send simple edits to lightweight models while reserving deep reasoning workflows for complex architecture decisions. This balances efficiency with maintaining quality.

Task tier routing in AI model selection classifies work from trivial to high-risk levels and assigns suitable token budgets and model capabilities. This ensures each task receives appropriate resources without excessive token consumption.

Yes, context management compresses skill chains by pruning stale context and reusing cached information to avoid redundant processing. This directly reduces unnecessary token usage during complex AI workflow execution.

Yes, you need integration with orchestration systems to apply token budget control effectively. The system requires token tracking and tier-based routing rules to dynamically minimize costs while maintaining output quality.

You should avoid dynamic task routing when your AI workflows lack consistent complexity tiers or when tasks require uniform deep reasoning. The system relies on classifying work from trivial to high-risk levels to function properly.