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Testing

From Monoliths to AI Proxies: Real-World Strategy for Testing and Evolving LLM Integrations

Ai Software Architecture Application Development Devops Software Architecture LLM Proxy Pattern AI Architecture Best-Practices Large Language Models LLM Integration Model Context Protocol (MCP) Testing Testing AI Systems
Integrating Large Language Models (LLMs) into production systems presents unique architectural, testing, and operational challenges. This article shares practical insights and solutions from real-world experience integrating LLMs into a customer interaction platform. It covers the evolution from a monolithic to a more modular AI Proxy architecture pattern, strategies for testing, deploying and monitoring LLMs, and the emerging Model Context Protocol (MCP) standard. Application developers and software architects will learn proven practices to build robust, reliable and responsible LLM-powered systems.

Running Testcontainers On Dynamic Ports

Programming Kotlin Docker Testing

Running integration tests locally with Docker can be challenging when fixed ports are unavailable due to conflicts. This issue is compounded in shared CI environments where multiple workers are in use. However, using testcontainers can help overcome these obstacles by enabling the startup of Docker containers that listen on random ports.