Dynamic Docker Resource Scaling Architecture: LLM-Driven Urgency Analysis for Multi-Agent Financial Systems

Abstract

This article presents a groundbreaking framework for Dynamic Docker Resource Scaling Based on LLM-Inferred Urgency, specifically designed to address the critical challenges of resource management in financial multi-agent systems. The integration of large language model-driven multi-agent systems has revolutionized financial services, enabling sophisticated workflows for customer support, fraud detection, and regulatory compliance, yet traditional container orchestration frameworks fail to meet the dynamic demands of these applications. The proposed framework leverages the semantic understanding capabilities of LLMs to analyze incoming financial queries and infer their urgency, enabling proactive resource allocation that aligns computational resources with business priorities. Built on the LangGraph platform, the system comprises three core components: an LLM-based Urgency Analyzer that classifies queries based on semantic cues and contextual indicators, a Dynamic Resource Manager that interfaces with Docker Engine to adjust container resources in real-time, and a persistence layer that maintains state across distributed workflows. The framework implements a hierarchical priority model with multiple urgency levels, enabling granular control over resource allocation while maintaining sub-second response times. Through comprehensive evaluation across real-world financial applications, including fraud detection, customer support prioritization, and regulatory compliance reporting, the framework demonstrates significant improvements in latency reduction, resource utilization efficiency, and cost optimization compared to traditional metric-based scaling approaches. The research establishes a new paradigm for AI infrastructure in financial services, where semantic-aware scaling enables intent-driven resource management that fundamentally transforms how institutions deliver customer experiences in an increasingly AI-powered landscape.