The Model Doesn't Matter. The Harness Does.
📰 Dev.to AI
The performance of AI models is less important than the harness or framework they're deployed in, as the same model can have vastly different results in different agent platforms
Action Steps
- Evaluate the performance of different agent platforms using the same model
- Compare the results of the same model in different harnesses
- Consider the impact of the harness on the overall performance of the model
- Choose an agent platform based on its harness, not just the model it runs
- Test and validate the performance of the chosen agent platform
Who Needs to Know This
Developers and engineers working with AI models can benefit from understanding the importance of the harness, as it can significantly impact the performance of their models. This knowledge can help them make informed decisions when choosing an agent platform
Key Insight
💡 The harness or framework an AI model is deployed in can have a much greater impact on its performance than the model itself
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The harness matters more than the model! Same model, different agent platforms, vastly different results #AI #MachineLearning
Key Takeaways
The performance of AI models is less important than the harness or framework they're deployed in, as the same model can have vastly different results in different agent platforms
Full Article
The Model Doesn't Matter. The Harness Does. Six frontier coding models now score within 0.8 points of each other on SWE-bench Verified. The same model wrapped in different agent frameworks swings almost ten points on SWE-bench Pro. Picking an agent platform based on which model it runs misses where the real performance differences come from. The useful shift in framing came from Birgitta Böckeler at Thoughtworks, writing <a href="https://martinfowler.com/articles/harness-eng
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