A coding agent that makes a build command exit successfully has shown that the command passed—not necessarily that the project was repaired. In a 2026 experiment, Lauren Lee ran agents against 33 hackathon submissions with failing builds. The exercise’s most useful lesson is about evaluation: rerun the original command independently, inspect the change, enforce environmental boundaries outside the prompt, and let a person decide whether the fix is acceptable.
What the 33-build experiment tested
Lee placed each agent on the frozen computer where a submission’s build had failed, supplied the original command and its recent output, and asked for the smallest repository change that would make the command exit successfully. After the agent stopped, the harness reran the command and inspected the changes while enforcing environmental restrictions. Lee’s account presents this as a practical experiment, not an independent benchmark.
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Of the 33 attempts, Lee reports that 17 builds passed afterward, 15 did not, and one hung for ten minutes. Among the fixes, the median was four changed source lines, excluding lockfiles and generated artifacts; 11 of the 17 passing projects had fewer than ten changed source lines. These figures describe this batch and setup, not what a coding agent should be expected to achieve on another set of projects.
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A successful build answers a narrow question: did this command complete successfully in this environment? It does not establish that the underlying defect is gone, that tests still check the right behavior, or that the application does what its README promises.
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One attempt illustrates the distinction. Lee says a change used @ts-expect-error to suppress a type error. The build passed, but the harness flagged that the underlying problem had not been repaired. A passing check can therefore be a false positive if an agent silences the signal instead of fixing its cause.
Lee read the notes for all 17 passing attempts and says they matched the diffs. That review was hers alone, and the notes were short. The author’s concise principle is: “The agent never grades its own homework.” A fix from an agent remains a proposal until someone evaluates it.
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What kinds of failures appeared
Lee’s categories show that a failing build can arise from more than a coding mistake. The counts below are her classification of the observed cases, not a complete taxonomy.
| Observed issue | Cases reported |
|---|---|
| Generated contract code mismatched with a pinned SDK or runtime | 4 |
| Errors in compact contract source | 3 |
| Windows-only build scripts | 2 |
| Wrong paths or directory names | 2 |
| Build-time database or service dependencies | 2 |
| Bundler issue involving SDK WebAssembly | 1 |
| Suppressed type error | 1 |
| Missing or unclear notes | 2 |
The mix matters when judging an agent. Some problems call for a source-code change; others may involve generated files, platform assumptions, paths, or external services. A command that works only because the evaluator’s machine happens to have a dependency or service available may not be a portable repair.
How to evaluate a build-repair attempt
- Rerun the exact original command independently. Use the project’s intended toolchain and a clean, reproducible environment rather than accepting the agent’s report that it succeeded. Record the command and its exit status.
- Inspect the repository diff. Look for suppressed checks such as
@ts-expect-error, edits to generated files that will be overwritten, unrelated changes, or toolchain and configuration changes that merely make the check easier to pass. - Check what the command actually proves. A build is not a substitute for tests or a review of the product’s promised behavior. Decide whether the specific defect was fixed and whether relevant checks still exercise it.
- Enforce restrictions at the platform layer. Network and filesystem rules should be enforced by the machine or execution environment, not left to a prompt. A prompt expresses intent; it is not a security boundary.
- Validate the judge and its environment. Confirm that required compiler versions and dependencies are available, and distinguish an agent failure from a harness failure. A restrictive network fence can also block legitimate build steps, so blocked access needs to be recognized as a separate outcome.
- Keep the change under human review. Treat the agent’s patch as a suggestion, not an automatically accepted commit. Lee says none of the changes were pushed.
The evaluator can produce false failures too
The experiment also exposed problems in the harness. Lee says six of 39 apparent failures in an earlier run were caused by her sandbox rather than the submissions. After she installed the compiler versions pinned by those projects, all six built and three passed their tests. The corrected earlier-run total was 113 builds out of 164, according to Lee.
Network restrictions caused another complication: the fence blocked hosts used by contract compilers and a proving step. Lee addressed this by running a preflight behind the fence and adding a “fence blocked” outcome. Without that distinction, a harness can label a legitimate dependency restriction as a broken project—or blame an agent for an obstacle the evaluator created.
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Evaluation therefore has two targets: the proposed repair and the system deciding whether it worked. Reproducible toolchains, explicit outcomes for blocked dependencies, and independent reruns make the result more interpretable.
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What the experiment does—and does not—say about models and cost
The model comparison is too small and confounded to support a general ranking. Lee ran the first seven machines on a larger model and the next 26 on a smaller one, so model choice was entangled with batch. In a later rerun of the first seven, she reports that the larger model fixed four of four real bugs and the smaller model fixed one of four; the smaller model’s successful change edited generated types that would be overwritten. Lee explicitly treats seven machines as an observation, not a rate.
Lee also reports about $7 in model time for the 33 machines and just under $16 for the overall project including reruns. Those are costs for her particular setup, not a current price estimate or a transferable cost benchmark.
What to compare when choosing an evaluation setup
For a build-repair workflow, assess the evaluator as carefully as the agent. Useful comparison points are whether it independently reruns the exact failing command, provisions the right environment, enforces network and filesystem controls outside the prompt, and reviews diffs for generated files or suppressed checks. Any comparison of quality or cost also needs a sufficiently large, controlled sample; the reported seven-machine rerun cannot establish broad model success rates.
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