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The HMS Blog

Current topics, practical solutions, and fresh ideas: On the HMS blog, you'll learn how companies successfully use data and analytics.

How can legacy systems be modernized securely and in a planned manner?

Successfully modernizing legacy systems doesn't start with migration—it starts with transparency. Learn how automated code analysis and AI-powered assessments reduce risks, enable informed decisions, and pave the way for AI-ready software.

Dennis Stolp

Harness Engineering

AI coding agents are currently transforming the way software is developed. They plan tasks, write and modify code, run tests, and respond to feedback. In practice, however, it quickly becomes clear that a powerful model alone does not make for a reliable agent. For an agent to operate securely, transparently, and reliably, it needs the right environment. This environment is called a harness. The task of building, operating, and continuously improving it is known as harness engineering.

Gianni Gagliardi

Luis Wirth

Modernization Instead of Migration

Migration sounds like moving. But that is precisely the problem today. If you simply translate legacy code 1:1 into a new language, you carry over architectural problems from the past. The result: modern code built on a foundation that wasn't designed for AI agents.

 

Dennis Stolp

Agent-to-Agent Protocol: When Companies Should Use Multi-Agent Systems

This article helps enterprise teams determine when A2A is the right level of collaboration, when MCP or APIs are sufficient, and what governance requirements must be met before implementation.

Fabian Wahren

Why AI-Powered Code Migration Doesn't Scale Without Agent Systems

Individual LLMs deliver impressive results. However, when modernizing large legacy environments, analysis, orchestration, testing, and governance are what determine success. Read this article to find out why agent-based systems are key to this process.

Dennis Stolp

Inside Forecasting: What Really Sets Strong Forecasting Applications Apart

Why Forecasting Is Rarely Just a Modeling Issue: This article explores the factors that determine the acceptance and value of forecasts in practice—from explainable AI to the integration of local data.

Kilian Schneider