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Coding agents can produce code that looks nearly finished and still miss a requested requirement, test only the cases their implementation already handles, break existing behavior, or rely on an unchecked assumption. The last mile is the work of turning that plausible implementation into a change that meets the full request, preserves what should not change, and has credible evidence behind it.
Why coding agents stumble at the last mile
“Last mile” is a useful description of near-miss failures, not a standardized benchmark or guarantee about every agent. In a 2026 study of 1,700 expert-built coding tasks—1,000 repository tasks and 700 terminal tasks—Surge AI authors analyzed four recurring failure modes: lost requirements, narrow testing, silent regressions, and weak ground truth. The study’s abstract describes agents that build most of a feature but drop a requirement, test only cases their implementation handles, break behavior that should remain intact, or validate against an unchecked assumption. These are the authors’ findings for their evaluated tasks, not an estimate of failure rates across all coding-agent use. Read the paper.
A near-miss can still block acceptance
Passing most checks is not the same as satisfying the request. In one example in the study, a missing requirement caused 16 of 137 target tests to fail. Among the paper’s 83 failed in-house DeepSWE base runs, 59% passed at least 80% of target tests, and the median failed run passed 86%. Those figures describe that sample only. They illustrate why a polished diff or a high partial test score can conceal a consequential omission.
Feature completion and regression safety are separate
A change has two jobs: add the requested behavior and retain behavior that should remain unchanged. In those same 83 failed runs, 84% preserved every pass-to-pass test. That finding distinguishes many feature-completion misses from regression failures in the study; it does not show that regressions are unimportant. The authors’ training reward assigned zero to a rollout if any pass-to-pass test regressed, even when target checks earned partial credit.
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How to check whether an agent actually finished
Do not use the agent’s completion summary as proof. Treat it as a report to verify against requirements and independent checks. A practical workflow, synthesized from the study and a separate scientific-computing field report, is:
- Recover the full request. Turn it into a checklist covering requested behavior, interfaces, formats, edge cases, constraints, and behavior that must remain unchanged. Resolve ambiguities before accepting the implementation.
- Map each requirement to evidence. For every checklist item, identify at least one test or other observable acceptance check. Include alternate inputs and negative cases, not only the path the current implementation appears to support.
- Protect existing behavior. Run the relevant existing regression suite and add checks for important behavior that the change could affect. Review failures as well as passes: a new feature can work while an unrelated contract has quietly broken.
- Set up independent ground truth. If there is no exact expected output, define acceptance criteria before judging the result. Use an independent reference, emulator, or controlled inputs with known properties where possible.
- Gate work in stages. Run tests or benchmark harnesses at meaningful intermediate points, inspect discrepancies, and decide whether the final evidence supports the claim that the change is complete.
- Review the evidence and the change. Check that the tests exercise the requested behavior rather than merely confirming the implementation’s assumptions. Have a human inspect the result when the consequences or software surface warrant it.
This is a practical synthesis, not a formally validated universal protocol. Its key discipline is to make acceptance depend on evidence tied to the request, rather than on how complete the code looks or how confidently the agent describes it.
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What changes when there is no exact expected answer?
For many tasks, a test can compare output with a known answer. Scientific computing and other exploratory work may lack such an oracle: there may be no single exact result to compare against. That does not make validation optional. Define what must remain true, then seek evidence that does not depend on the agent’s own claim.
Do these 3 things before closing this tab:
1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsA 2026 exploratory field report covering eight agentic coding projects in scientific computing describes validation with simulated or synthetic data whose properties were known when exact reference outputs were unavailable. Contributors used staged feedback loops and intermediate test or benchmark harnesses. The report says larger software surfaces and changes to scientific behavior raised the human validation burden; contributors remained the principal adjudicators of success in all but one of the projects. It is an exploratory account, not a controlled estimate for software development generally. Read the field report.
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- Use controlled cases. Construct inputs for which relevant properties are known, then check whether the change preserves or produces those properties.
- Use an independent reference where available. A reference implementation, emulator, or separately derived result can expose assumptions shared by the agent’s code and its tests.
- Make acceptance criteria explicit. When multiple outputs may be valid, state which properties or constraints determine success before inspecting the result.
- Keep human judgment in the loop. A person may need to interpret whether the evidence supports the intended scientific or product claim, especially when behavior itself changes.
What the 2026 coding-agent results establish—and what they do not
Mehta, Ritchie, and Chen evaluated Kimi K2.7 Code before and after one reinforcement-learning run on expert-built tasks. Repository tasks used hidden fail-to-pass tests for requested changes and pass-to-pass tests for existing behavior; terminal tasks used expert-written hidden verifiers. The authors report improved pass@1 on six external benchmarks:
| Benchmark | Before training | After training |
|---|---|---|
| SWE-Bench Pro | 60.1% | 64.8% |
| DeepSWE | 31.0% | 43.4% |
| Terminal-Bench 2.1 | 67.4% | 82.0% |
| Terminal-Bench 3 | 1.4% | 12.1% |
| Terminal-Bench 4 | 0.0% | 7.6% |
| SWE-Marathon | 5.0% | 25.0% |
These are the authors’ reported results for one checkpoint and training recipe; their own evaluations report pass@1 from a single run per benchmark. Some baselines were publicly reported rather than rerun in-house, and the public DeepSWE baseline differs from the authors’ in-house run. Terminal-Bench 4 revises Terminal-Bench 3, so the authors count that family once in pooled analysis. They report gains of 4.7 to 20.0 percentage points across the six benchmarks, with task sets, evaluation harnesses, and sample sizes differing. They report a statistically significant pooled improvement across five independent task sets (p < 0.001), and across the three independent task sets released after training-data collection (p = 0.004). This evidence concerns the evaluated setup; it is not a production-quality guarantee, nor a direct comparison of commercial coding-agent products.
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What to look for in a last-mile workflow
When evaluating a team process or an agent’s verification workflow, ask how it handles the whole chain from specification to evidence:
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- Requirement coverage: Can each requested behavior and constraint be traced to a check?
- Independent testing: Do tests challenge the implementation with alternate and negative cases, or merely confirm the route it already takes?
- Regression protection: Are existing contracts and behavior tested alongside the new feature?
- Ground truth: If there is no oracle, are acceptance properties, controlled inputs, or independent references defined?
- Transfer: Does evidence cover the relevant task types and harnesses, rather than assuming success transfers from a different benchmark?
- Human validation: Is a reviewer involved where software breadth, domain judgment, or scientific behavior makes automated checks insufficient?
- Evidence quality: Is completion supported by hidden or representative tests and review, or only by the agent’s self-assessment?
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