The Tool Desk
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What counts as the model, and what counts as the scaffold?
The model generates reasoning and selects actions. The scaffold, also called a harness, is the surrounding software and evaluation setup: it determines what context the model receives, which tools it can call, how steps and state are managed, what feedback comes back, and how completion is judged.
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Those parts interact. A capable model may perform poorly if it receives incomplete context, lacks suitable tools, or is stopped by a flawed completion rule. A stronger scaffold may improve tool use, recovery, or verification, but it cannot guarantee that a model will reason correctly or execute a task successfully. These are explanations to test in a specific system, not universal outcomes established for every agent.
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Why a benchmark score belongs to a configuration
Benchmarks test particular combinations of models, scaffolds, tasks, budgets, and graders. OpenAI’s MLE-bench, for example, identifies its best-performing setup as o1-preview paired with AIDE scaffolding. That result is not a context-free score for o1-preview.
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In OpenAI’s 2024 report, that setup reached at least the level of a Kaggle bronze medal in 16.9% of MLE-bench competitions. This is a result for the named setup and benchmark task set; it should not be read as a general success rate for agents.
Scaffolding also matters when interpreting software-engineering results. OpenAI’s SWE-bench Verified article discusses scaffold differences and external enhancements as relevant to capability assessment. Its historical leaderboard context, dated August 5, 2024, reported 20% for top agents on SWE-bench and 43% on SWE-bench Lite. Those figures describe that date’s context, not current leaderboard standings or a direct comparison of model-only performance.
Why long tasks need their own measurements
Short tasks can miss failures that emerge only when an agent must preserve state, recover from errors, or complete many dependent steps. METR’s long-task work proposes measuring the length of tasks agents can complete. Its 2025 paper estimates that, over the selected tasks and measurement method covering 2019–2024, the task length frontier AI systems could complete with 50% reliability doubled approximately every seven months. That is a historical trend estimate, not a forecast or evidence that scaffolding alone caused the trend.
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How to compare two agent configurations fairly
- Define the task and success criteria. Use the same task set and task-specific checks for each configuration, and specify what counts as correct and complete.
- Hold the environment steady. Keep tool access, environment, and available budget constant while testing. Where practical, change one factor at a time—such as the model or scaffold—rather than changing both.
- Report the full setup. Name the model and scaffold together, along with the conditions that affect the result. MLE-bench’s reported best result is a pairing, not a model name alone.
- Test varied task lengths. Include longer tasks as well as short ones; record task duration or length so that a completion rate has meaningful context.
- Audit the work, not just the pass signal. Inspect trajectories and outputs for silent omissions. OpenAI’s o1 system card reports that manual inspection found some trajectories that passed an autograder had major portions of the task silently incomplete.
- Repeat runs and record trade-offs. Compare completion, duration, process quality, efficiency, and failure behavior. Report time or token and compute costs when available, and check whether outcomes reproduce across runs.
There is no single common measurement protocol across all agent domains in these sources. A leaderboard rank or pass rate alone cannot establish that one component is the bottleneck: the result needs to be interpreted against its tasks, checks, and tested configuration.
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If changing the scaffold improves performance while the model and other conditions remain fixed, that is evidence that the scaffold was limiting performance on those tasks. If changing the model helps under a fixed scaffold, the model was a constraint in that test. Neither result proves that the same component dominates on different tasks or in a different environment.
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OpenAI’s SWE-bench Verified article puts the evaluation concern plainly: “Community-led progress in agent scaffolding highlights the need to consider potential external enhancements to a model when assessing risk.” That is a reason to describe the complete tested system when reporting capability, rather than attributing a result to the model alone.
The available evidence is strongest for coding and machine-learning engineering agents. It supports treating performance as configuration-specific and evaluating longer tasks carefully; it does not establish a universal ranking of model versus scaffold across all kinds of long-running agents.
Quick Recap
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