Your harness is failing your agent but there's no benchmark to prove it

Reddit r/AI_Agents News
ai-agents mcp tool-use benchmarking reliability error-handling

Summary

The article highlights a lack of benchmarks for evaluating the reliability of agent harnesses, specifically focusing on how MCP implementations handle tool calls and errors compared to the models themselves.

You can compare models on function calling, multi turn tool use, schema adherence. Basically, there's a good amount of public data at the model layer. Then why can't I find reliability data at the harness layer? Not which models calls tools best, which harness implementation handle malformed tool responses without silently swallowing the error, which ones retry in ways that fix the problem rather than amplify it, which ones surface failures in a format the model can actually reason about. I moved to MCP as the default integration layer and started treating the MCP server as infrastructure. But from what I've seen, the quality of MCP implementation varies more than we want to admit. The model gets blamed for bad tool call behavior, but a lot of the time the failure is in the handling layer underneath it. Anyone stress testing the actual implementations rather than just the models on top of them?
Original Article

Similar Articles

Stop Comparing LLM Agents Without Disclosing the Harness

arXiv cs.AI

This position paper argues that in long-horizon LLM agent tasks, the execution harness often determines performance more than the model itself, and current benchmarks misattribute harness-level gains to model improvements. It proposes a harness-aware evaluation framework with disclosure standards and variance decomposition protocols.