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

A new arXiv paper (2608.17188v1) presents a practitioner framework for token optimization and context-window management in multi-agent AI workflows, reporting a 60-70% token reduction and cutting cold-load latency to 61-116 seconds from a baseline of 3.5-10.5 minutes in production. The study, based on an internal production dashboard that extracts structured work items from meetings, email, and chat, describes six patterns including context stratification and semantic caching, and found that a 50:50 signal/noise context condition improved relevance accuracy by +0.077 over a 100% condition (Cohen's d = 0.49, Holm-adjusted p < .001).

arXiv:2608.17188v1 Announce Type: new Abstract: Multi-agent AI workflows are limited not only by model quality but by token cost, latency, and context-window quality. This paper presents a practitioner framework for token optimization and context-window management, grounded in an internal production dashboard that extracts structured work items from meetings, email, and chat with LLMs and routes summaries across workstreams. Six patterns are described: context stratification, fetch-once/process-locally architecture, schema-contracted prompts, token-aware fallback chains, semantic caching, and inter-agent communication compression. In production they cut measured cold-load latency to 61-116 seconds six timed runs from an operational baseline of roughly 3.5-10.5 minutes, with an estimated 60-70% token reduction. It also reports a controlled context-composition study: 2,420 confirmatory trials across 11 model configurations, using 661 anonymized workplace items scored for relevance. Holding the prompt at a fixed ten items, replacing some high-relevance items with same-domain low-relevance items improves the model's relevance-score concordance on the target items, versus high-relevance items only; we call this relevance-contrast context. In the all-11 paired analysis, the 50:50 signal/noise condition improved relevance accuracy by +0.077 over the 100% condition naive 95% CI +0.056, +0.098 , Cohen's d = 0.49, Holm-adjusted p < .001, n = 220 . These cells are not independent; by the nine model families the effect is +0.084 95% interval +0.064, +0.103 , reported as a within-corpus descriptive comparison, not a population inference. A Fusion-of-N follow-up found that learned synthesis did not beat the mechanical set union of item IDs. The contribution is a measured engineering layer between model research and production agent practice: repeatable patterns and evaluation methods for faster, cheaper, more reliable workflows.