Six Agent Harness Capabilities for Higher Model Performance
Source
Michelle Horton
Author
Michelle Horton
Date
Terms in this piece · Glossary
agent harness — The scaffolding around a model that turns it into a working agent — the loop, the tools it can call, and the rules for when to stop.
context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
Why it matters
Most AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → quality problems are agent harnessThe scaffolding around a model that turns it into a working agent — the loop, the tools it can call, and the rules for when to stop.Full definition → problems misdiagnosed as model problems.
Key quotes
“Harness design alone can account for double-digit swings in benchmark results and significant differences in token cost, with the same underlying model.”
Ricardo Silveira Cabral and Paul Furgale
“NOOA takes a simpler approach: an agent is a single Python class . Its methods are its capabilities. Its fields are its state. Its docstrings are its prompts. Its type annotations are enforced contracts.”
Ricardo Silveira Cabral and Paul Furgale
“On SWE-bench Verified, NOOA reaches 82.2% with GPT-5.5 above the published leaderboard SOTA at submission (79.2%)—and 79.8% with Opus 4.6, using a general-purpose, 253-line agent with no benchmark-specific prompts.”
Ricardo Silveira Cabral and Paul Furgale
“With GPT-5.5, NOOA reaches 82.2% on SWE-bench Verified using 29 LLM calls and ~1.1M tokens per task. The comparison harnesses need 66 calls and 2.2M tokens to reach 78.2%, and 29 calls at 1.3M to reach 78.6%. Parity or better, at roughly half the cost.”
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