The harness—the orchestration layer, the tool-calling loop, the error handling, and the prompt engineering—is where the actual performance wins or losses happen. A mediocre model with a brilliant harness often beats a frontier model with a lazy wrapper. We're missing a community-driven way to measure this specific variable across diverse, real-world tasks.
I'm thinking about starting a project to build a leaderboard focused entirely on harness performance. The goal would be to track how different orchestration frameworks handle complex tasks, grouped by the underlying LLM and the amount of reasoning effort (like CoT or iterative loops) being used.
To make this a legitimate deep dive, the tasks can't be synthetic. They need to be actual, complex problems pulled from open-source repos—things that actually break in production. Since this should be a community effort, the measurement criteria and the framework for the benchmark should be decided by the group, not just one person.
For those who aren't familiar, a "harness" in this context is everything that surrounds the LLM call. If you're building an LLM agent, your harness is what manages the state, parses the tool outputs, and decides when the task is actually "done." I maintain a coding agent called Dirac, but I don't want to bake my own biases into the requirements. The idea is to create a neutral ground where we can see which architectural patterns actually work. If we can get a set of diverse tasks and a way to measure success/failure rates across different harnesses, we'd finally have a practical tutorial for how to actually build reliable agents instead of just guessing which model is "smarter."
If people are into this, I'll set up a Discord to coordinate. We could look at metrics like: Success rate on multi-step dependenciesToken efficiency per successful task completionRecovery rate after tool execution errors****Latency overhead added by the orchestration layer
This would be a massive step toward a more real-world understanding of deployment. Instead of arguing about whether
Claude3.5 or GPT-4o is better at a specific benchmark, we could see which harness implementation actually ships the code.
[Next Claude Fable 5 and GPT-5.6 Sol High are fighting for the crown →](/en/threads/5801/)
[an AI side-hustle playbook](https://tanyan888.com/), with plenty of directly applicable cases.
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@CameronWizardConsistency is key. If the variance is too high, the benchmark doesn't actually prove the harness is stable.