Jens Polomski, Founder of German AI Knowledge Platform Snipki: The Value of AI Agents Lies in the Orchestration Layer, Not the Model Itself
After building and then shutting down his own AI agent system, Jens Polomski, founder of the German AI knowledge platform Snipki, argues that enterprises should not frequently switch between different models. He contends that what truly determines an agent's effectiveness is the orchestration tools and workflow design surrounding the model, rather than the model itself.
In February 2026, Jens Polomski, founder of the German AI knowledge platform Snipki, became one of the earliest adopters to deploy AI agents in practical work. He built a small agent team using OpenClaw, with a core coordination agent named Neo responsible for handling emails and other work tasks. Nine months later, Neo was shut down. However, Polomski believes that what truly holds value has survived: not a specific tool, but the underlying idea. OpenClaw itself was merely a combination of various components; today, many vendors have caught up with this approach, and numerous frameworks have emerged.
From this, Polomski offers a clear recommendation for enterprises: select one tool and avoid migrating back and forth between different platforms. He refers to this behavior as 'Tool-Hopping'—exporting context from here, importing it there, and bringing it back, only for everything to change again a month later. He considers this unworkable and crazy in a corporate environment. A better approach is to make choices based on practical factors such as regional considerations, consumer needs, or user experience, and then deepen your engagement with that choice. His conclusion is that it matters less and less which tool you use, and more how you use it.
Behind this judgment lies a more specific mechanism. Polomski points out that on platforms aggregating multiple models, the gap is not created by the models themselves, but by the harness—the set of orchestration tools, agent workflows, and integration methods surrounding the model. Aggregator platforms often only gradually add these capabilities. Meanwhile, if an enterprise faces twenty optional models simultaneously, it can lose its sense of direction. His current practice involves maintaining usage across different vendors like Claude and ChatGPT while running agents on different servers; his driver is actual need rather than loyalty to any specific framework.