KDnuggets Details 4 Strategies to Cut Multi-Agent AI Token Use

1 articles · Updated · KDnuggets · Aug 3

Four tactics anchor the guide: static instruction caching, semantic caching, just-in-time tool loading, and task escalation that routes simple work to cheaper models.
Token sprawl in multi-agent systems comes from repeated system prompts, memory logs, and oversized tool specifications, driving both latency and compute costs higher.
Semantic caching uses embeddings to match similar intents—such as two ways of asking to reset a router—and can sometimes return a prior answer without calling an LLM.
A 0.90 similarity threshold in the article’s sample code pairs cache lookup with model routing, sending short or simple requests to a local zero-cost model and escalating harder tasks to a heavier reasoning agent.
The broader takeaway is that multi-agent AI can scale without matching cost growth if developers trim prompt repetition, load tools only when needed, and reserve large models for complex jobs.