Composing Cost-Reduction Techniques in Multi-Agent LLM Systems: A Practitioner's Empirical Study

The rapid deployment of multi-agent large language model (LLM) systems in production settings has surfaced a critical challenge: computational cost and latency at scale. A single complex task routed through a multi agent pipeline combining retrieval augmented generation (RAG), tool use, and inter-agent communication, can incur dozens of LLM API calls, making real world deployment economically prohibitive. The research community has proposed several cost reduction techniques: semantic caching, model routing, and early stopping. Each has been shown to reduce cost in single agent or tw -stage settings. However, no prior work has examined how these techniques interact when composed together in a realistic multi-agent pipeline. We present an empirical study comparing four pipeline configurations, baseline, caching-only, routing-only, and combined — across a standardized task suite using LangGraph for orchestration, LiteLLM for model routing across GPT-4o, Claude, and Llama-3, and LangSmith for cost and latency observability. We measure total token usage, wall-clock latency, cost per task, and task success rate across configurations. Our preliminary findings reveal non-additive interaction effects: technique composition does not uniformly compound savings and introduces failure modes including cache inconsistency across chained agents and routing degradation on multi step tasks, not observed in single agent settings. From these findings, we derive practitioner design guidelines for cost aware multi agent system design. This work bridges a gap between isolated technique evaluation and production-realistic deployment, offering the first characterization of cost-reduction technique composition in multi agent LLM pipelines