Token Optimization and Context Window Management in Multi-Agent AI Workflows

The paper "Token Optimization and Context Window Management in Multi‑Agent AI Workflows" introduces a practitioner framework that reduces token usage and latency in multi‑agent AI systems. It outlines six patterns—context stratification, fetch‑once/process‑locally architecture, schema‑contracted prompts, token‑aware fallback chains, semantic caching, and inter‑agent communication compression—and reports a 60‑70% token reduction and a 61‑116 second cold‑load latency improvement in production. A controlled study on relevance‑contrast context shows that mixing high‑ and low‑relevance items in prompts can improve relevance accuracy by up to +0.084. whyItMatters":"The work provides concrete, repeatable engineering patterns that bridge research and production, enabling faster, cheaper, and more reliable AI workflows."

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