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

Multi-agent AI workflows are increasingly 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 in multi-agent AI systems, grounded in an internal production dashboard that ingests meetings, email, and chat content, extracts structured work items with LLMs, and routes summaries across multiple workstreams. Six optimization 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 the production pipeline, these patterns reduced measured cold-load latency to 61-116 seconds (six timed runs) from an operational pre-optimization baseline of roughly 3.5-10.5 minutes, and historical token analysis estimates a 60-70% token reduction. The paper also reports a controlled context-composition study: 2,420 confirmatory trials across 11 model configurations, using 661 anonymized workplace communication 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, compared with providing only high-relevance items - an effect we call relevance-contrast context. In the all-11 paired analysis, the 50:50 signal/noise condition improved relevance accuracy by +0.077 over the 100% signal condition (naive 95% CI [+0.056, +0.098], Cohen's d = 0.49, Holm-adjusted p < .001, n = 220). Because these 220 configuration-by-block cells are not independent, the effect is also summarized by the nine model families (+0.084, 95% interval [+0.064, +0.103]) and reported as a within-corpus descriptive comparison rather than a population inference. A follow-up Fusion-of-N study found that learned synthesis of repeated extraction samples did not beat the mechanical set union of extracted item IDs. The contribution is a measured engineering layer between model research and production agent practice: not a new model architecture, but a set of repeatable patterns and evaluation methods for making agent workflows faster, cheaper, and more reliable. This is independent research conducted in a personal capacity. A companion technical appendix (included in this record) provides full evidence tables and robustness detail.