A coding agent’s harness can make a measurable difference—but the best design depends on the model, task, and available context. In a 2026 study of one harness across four models and two benchmarks, context management helped most when context windows were tight; planning and tool-interface results varied by model and task. The findings are evidence about these tested configurations, not a ranking of commercial coding agents.
What the study tested
Run-Ze Fan and eight coauthors’ An Empirical Study of Harness Design for Coding Agents, published September 17, 2026, reports 176 matched settings examining three harness components: persistent planning, the agent’s action interface, and context management. The authors held a lightweight ReAct-style execution loop fixed while varying those components.
The evaluation used Nemotron-3 30B, 120B, and 550B, plus Mistral-Medium-3.5-128B. It covered 500 SWE-Bench Verified tasks involving Python repositories and 89 Terminal-Bench 2.1 tasks. Context policies were compared at nominal windows of 32k, 64k, 96k, and 128k tokens. Planning and interface comparisons were narrower ablations, run with the T4 context policy at 128k.
The tested context policies ranged from no compaction (T0) to output elision, recoverable external storage, LLM-generated summaries, and a staged policy (T4) that elides stale output before selectively summarizing. The structured interface provided file, search, web, and shell tools; the comparison interface exposed bash alone.
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When did context management help?
Its clearest benefit appeared at 32k tokens, where trajectories often ran out of room without management. Fan et al. report mean success-rate gains for managed context tiers over no management of 35.7 percentage points on SWE-Bench and 9.5 points on Terminal-Bench at 32k. At 128k, the corresponding gains were 2.7 and 2.8 points.
| Nominal context window | Benchmark | Managed tiers’ mean success advantage over no management | No-management overflow rate |
|---|---|---|---|
| 32k tokens | SWE-Bench Verified | 35.7 percentage points | 78.7% |
| 32k tokens | Terminal-Bench 2.1 | 9.5 percentage points | 61.0% |
| 128k tokens | SWE-Bench Verified | 2.7 percentage points | 8.7% |
| 128k tokens | Terminal-Bench 2.1 | 2.8 percentage points | 12.1% |
All four overflow rates and success advantages above are study averages across the tested settings at each budget. Every managed tier avoided overflow failures in the reported tests. The pattern suggests that management chiefly kept runs going when unmanaged context would fill, rather than consistently improving an agent’s immediate decision-making.
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Which policy looked most efficient?
T4—elision followed by selective summarization—had the lowest average cost at every tested context budget and the lowest mean cost in seven of eight model-benchmark combinations. Its success was broadly comparable to the other managed tiers, making it the strongest reported efficiency choice within this experiment, rather than a demonstrated universal winner.
Adding recoverable recall to elision did not produce a clear accuracy improvement in the matched comparisons. T2 beat T1 in 15 of 32, lost in 14, and tied in three; its equal-weight mean difference was -0.36 percentage points. Across 64 T2 and T4 settings, recall was never invoked in 56.3%. These results describe these configurations and do not establish that recall is unnecessary in other harnesses or workloads.
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Does a persistent plan improve coding results?
Planning had different effects depending on model and benchmark. It helped Nemotron-3 30B most clearly: success increased by 11.6 percentage points on SWE-Bench and 4.5 points on Terminal-Bench, while inference cost rose on both. On SWE-Bench, the model’s median trajectory dropped from 40 turns with planning to five without it; runs ending without an edit rose from 27.8% to 68.6% when planning was disabled.
For Nemotron-3 550B and Mistral-Medium-3.5-128B, enabling planning reduced SWE-Bench inference cost by about 30% and 32%, respectively, while success changed by -2.0 and -0.4 percentage points. Nemotron-3 120B showed no consistent effect. The authors’ interpretation is that a plan can keep a weaker model working toward an edit, while helping stronger models avoid redundant verification; the task family also matters.
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Are structured tools better than bash alone?
Neither interface won across all tested models and tasks. For Nemotron-3 30B, the structured interface increased success relative to bash-only by 15.0 percentage points on SWE-Bench and 10.1 points on Terminal-Bench. With bash-only, 66% of this model’s Terminal-Bench trajectories ended after calls incompatible with the available interface.
For Nemotron-3 550B, bash-only increased success by 3.6 points on SWE-Bench and 5.6 points on Terminal-Bench while reducing cost by 53% and 30%, respectively. Mistral’s results split by benchmark: structured tools improved SWE-Bench success by 23.2 points, while bash-only improved Terminal-Bench success by 6.7 points.
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This is not an isolated test of tool count. The structured-versus-bash comparison also changed interface instructions, file-state tracking, read-before-write enforcement, and automatic post-edit diagnostics. The results compare two complete interface designs, so they do not show that simply adding or removing tools would cause the same effects.
How to apply the findings without overgeneralizing
The study offers useful design signals, but it does not identify a single best harness. When weighing a harness choice, consider the interaction of these factors:
- Context pressure: Management mattered most at the tightest tested window, where unmanaged runs frequently overflowed. Its average success advantage was much smaller at 128k.
- Model capability and shell proficiency: Structured tools helped the tested Nemotron-3 30B, while bash-only was more efficient for Nemotron-3 550B in these comparisons. Mistral’s direction changed by benchmark.
- Task structure: Repository issue repair on SWE-Bench and command-line-oriented Terminal-Bench tasks did not always favor the same configuration.
- What you optimize: Success, inference cost, overflow, and trajectory length can point to different trade-offs. A lower-cost setup is not automatically the more successful one.
What the results cannot establish
The ablations do not cover every combination of components. Planning and action-space tests ran only with T4 at 128k, so their interaction with smaller windows or other context policies remains unknown. Each task was run once per setting, limiting what can be inferred about run-to-run variation. Terminal-Bench had 89 tasks, and many of its contrasts did not reach significance under paired McNemar analysis.
Some trajectory labels were assigned by LLM judges. The paper reports approximately 94.2% aggregate judge-human agreement and weighted mean Cohen’s kappa of 0.929, but those annotation checks do not remove the broader limits of task count, model selection, or benchmark coverage. The findings concern four models, two benchmarks, and one harness implementation—not all coding agents or real-world software work.
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