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Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Coding agents can produce a plausible, mostly complete change and still miss a requirement, test only the cases their own implementation handles, break existing behavior, or rely on an unchecked assumption. The last mile is the work of turning that output into a change that meets the request and has credible evidence behind it—not simply accepting the agent’s completion message.
Why coding agents can get close and still fail
A feature may look finished while one omitted interface detail, edge case, or constraint prevents acceptance. In a 2026 study of agentic coding tasks, one missing requirement led to 16 of 137 target tests failing. Among the study authors’ 83 failed in-house DeepSWE base runs, 59% passed at least 80% of target tests; the median failed run passed 86%. These figures describe that study sample, not coding-agent use in general. Mehta, Ritchie, and Chen’s 2026 paper groups recurring near-misses into four patterns:
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- Lost requirements: The agent implements the main request but misses a detail or constraint.
- Narrow testing: Checks cover the cases the implementation already handles, leaving alternate or negative cases untested.
- Silent regressions: The new change works, but behavior that should have stayed intact breaks.
- Weak ground truth: A test or validation depends on an assumption that has not itself been checked.
The same study found that 84% of its failed in-house base runs preserved every pass-to-pass test. In that sample, many failures were therefore incomplete feature work rather than regressions. Both kinds matter: passing old tests does not prove the new request is complete, and passing new checks does not prove existing behavior is safe.
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What the 2026 coding-agent study does—and does not—show
Mehta, Ritchie, and Chen evaluated Kimi K2.7 Code before and after one reinforcement-learning training run built around 1,700 expert-created tasks: 1,000 repository tasks and 700 terminal tasks. Repository tasks used hidden tests for requested changes and separate tests for existing behavior; terminal tasks used expert-written hidden verifiers. The reward allowed partial credit on requested-change checks but assigned zero if any protected existing-behavior test failed. That design makes the study’s distinction between completing a feature and preserving behavior explicit.
#1 Best Overall
The authors report higher pass@1 results after training 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 paper authors’ results for one checkpoint and training recipe, not a production-quality guarantee or a result that can be generalized to other agents. The authors report pass@1 from a single run per benchmark for their own evaluations; some comparison baselines were publicly reported rather than rerun in-house, and the public DeepSWE baseline differs from their in-house run. Terminal-Bench 4 revises Terminal-Bench 3, so the authors count that benchmark family once in their pooled analysis. They report statistically significant pooled gains across five independent task sets (p < 0.001), and across the three independent task sets released after training-data collection (p = 0.004). The reported improvement range of 4.7 to 20.0 percentage points spans benchmarks with different task sets, evaluation harnesses, and sample sizes.
How to close the last mile
Use the request as the source of acceptance criteria, not the implementation as the limit of what gets tested. The checklist below synthesizes practices described in the coding-agent study and an exploratory field report on scientific-computing projects; it is a practical guide, not a universally validated protocol.
- Turn the request into observable requirements. List expected interfaces, output formats, edge cases, constraints, and behaviors that must remain unchanged. Make ambiguous terms explicit before treating the work as complete.
- Give every requirement an independent check. Decide how to test each item, including alternate inputs and negative cases the current implementation might not handle. A check should assess requested behavior, not just confirm that the chosen implementation runs.
- Protect existing behavior. Run the relevant regression suite and add tests for behavior that the change must preserve. Keep new-feature checks and regression checks distinct so a pass in one category cannot conceal a failure in the other.
- Establish ground truth when exact outputs are unavailable. Define acceptance criteria in advance. Use an independent reference, emulator, controlled input, or simulated data with known properties where appropriate, rather than treating an unverified expected result as an oracle.
- Use staged gates and inspect discrepancies. Run intermediate tests or benchmarks during development, then evaluate whether the final evidence actually supports the completion claim. Do not rely only on an agent’s own summary.
- Have a person adjudicate consequential or broad changes. Review whether the checks represent the request and whether their results justify acceptance; this is especially important when a change affects scientific behavior or spans a large software surface.
When there is no exact expected answer
Some software work has no simple oracle: there may be no single correct output to compare against. That does not make validation optional. Define what properties a correct result must have, choose inputs whose expected properties are known, and check the result against an independent reference where possible.
A 2026 exploratory field report describes eight agentic coding projects in scientific computing. Its contributors used simulated or synthetic data with known properties when exact reference outputs were unavailable, alongside staged feedback and intermediate test or benchmark harnesses. The authors also report that larger software surfaces and changes to scientific behavior raised the human validation burden. The report is a field account, not a controlled estimate for software development as a whole.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why human review remains part of the workflow
In all but one of the eight projects described in the scientific-computing field report, contributors remained the principal judges of whether the work succeeded. The authors describe human work shifting toward specification, validation design, and interpretation. That is a useful framing for agentic development: delegate implementation where appropriate, but keep responsibility for deciding what counts as correct and whether the evidence demonstrates it.
Rank #4
An agent’s report can help a reviewer locate changed files, tests, or known limitations, but it is a claim to check—not proof of completion. The deciding evidence is whether independently designed checks cover the requirements, existing behavior remains protected, and any uncertainty in the expected result has been handled explicitly.
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