German Software Testing Practitioner Paul Behrend: Central Orchestration Agent Loads Skills on Demand, Replacing Simple Prompts and Multi-Agent Piling
Paul Behrend proposes using a central orchestration agent to dynamically load testing skills according to tasks while adhering to preset guardrails, replacing two failure modes of simple prompts and multi-agent piling; he also found that skill output quality is highly dependent on the underlying model, and switching models leads to significant bias.
The application of generative AI in software testing suffers from two common failure modes: simple prompts often produce incorrect or incomplete results, while piling up multiple independent agents increases cost and complexity without necessarily improving quality. Paul Behrend, who has over ten years of experience in software quality assurance, proposes an intermediate path in his practice report—deploying a central orchestration agent that guides the testing process according to preset guardrails and dynamically loads corresponding skills based on the current task, covering test analysis, test design, and test case generation separately. This integrates originally scattered auxiliary tools into a clear and verifiable workflow, producing reliable, traceable results that directly serve test implementation.
However, Behrend discovered a practical boundary when implementing this method: the output quality of skills is tightly bound to the language model they are adapted for. Once the underlying model is switched, the answer quality will show significant deviation. The orchestration architecture itself cannot shield against the impact of model changes; teams still face the risk of quality fluctuations when selecting and migrating models.
Behrend also points out that agents are not a universal replacement—in specific scenarios, classic automation remains the better choice. Ensuring test quality ultimately depends on experienced testers, clear processes, and targeted cooperation with agents; all three are indispensable.