PRAXIS: A Production Benchmarking Framework for Multi-Agent AI Systems

As multi-agent AI systems transition from research prototypes to production deployments, practitioners face critical decisions about model selection, agent configuration, and resource allocation that directly impact business outcomes. However, existing evaluation approaches focus narrowly on accuracy metrics without considering latency, cost, and scalability constraints that dominate real-world deployments. This paper presents PRAXIS (Production Real-world Agent eXecution Intelligence Suite), a systematic benchmarking framework designed to evaluate multi-agent system performance across the accuracy-latency-cost Pareto frontier under realistic production conditions. We introduce standardized task suites spanning reasoning, tool use, and collaboration scenarios, along with measurement methodologies that capture cold-start behavior, streaming latency, and throughput under load. Evaluation of 12 agent configurations across 5 foundation models reveals that optimal configurations vary significantly by task type, with accuracy-latency tradeoffs following predictable patterns amenable to intelligent routing. We demonstrate production impact through a Fortune 500 enterprise deployment achieving 68% reduction in operational costs while maintaining service-level agreements. Our framework enables practitioners to make informed deployment decisions: analysis identifies a 3.2x cost reduction opportunity through adaptive model selection without sacrificing accuracy. PRAXIS is released as open-source software with an interactive benchmarking dashboard.