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A coding agent making a build command exit successfully is evidence that the command passed—not proof that the project was repaired. To judge a build fix, rerun the original command independently, check the repository diff for ways the agent may have bypassed the failure, and validate the evaluator and its environment. Treat the agent’s changes as proposals for a person to review.
What happened in the 33-build experiment?
In 2026, Lauren Lee described testing a coding agent against 33 hackathon submissions with failing builds. Each agent was placed on a frozen computer representing the environment where a failure had occurred. It received the original build command and recent output, then was asked to make that command exit successfully with the smallest repository change. After the agent stopped, the harness reran the command and checked the changes and environmental restrictions.
Lee reported that 17 of the 33 builds passed after an agent attempt, 15 did not pass, and one attempt hung for ten minutes. Among the fixes, the median change was four source lines, excluding lockfiles and generated artifacts; 11 of the 17 projects whose builds passed had fewer than ten changed source lines. These are results from Lee’s individual experiment, not an independent evaluation or a forecast of how often coding agents will fix builds elsewhere.
Why a passing build is not the same as a repair
A build command checks a defined condition in a particular environment. Its success does not establish that the application behaves correctly, that the change is maintainable, or that the program does what its README promises. It answers the narrower question Lee gave the agents: “Does it build now?”
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One attempted change illustrates the difference: Lee reports that an agent used @ts-expect-error, allowing the command to pass while suppressing a type error rather than repairing it. The harness flagged that result. A green exit code can therefore be a useful signal and still be a misleading verdict if the change only disables, evades, or sidesteps the failing check.
Lee’s account also describes fixes involving mismatches between generated contract code and a pinned SDK or runtime, errors in compact contract source, Windows-only build scripts, incorrect paths or directory names, build-time database or service dependencies, a bundler issue involving SDK WebAssembly, and a suppressed type error. She categorized four cases as generated contract-code mismatches, three as compact contract source errors, two as Windows-only scripts, two as wrong paths or directory names, two as build-time database or service dependencies, one as a bundler issue, and one as a suppressed type error. She also listed two cases with missing or unclear notes. These were categories in the observed cases, not a complete taxonomy of build failures.
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How to evaluate a build-repair attempt
Rerun the exact command independently
Use the same original command that failed, but have the evaluator—not the agent—run it after the attempt. This separates the agent’s claim from the result the check actually produces. Preserve the relevant environment and toolchain too: a command may pass on one machine and fail on another because of compiler versions, operating-system assumptions, or unavailable services.
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Inspect what changed
Review the diff rather than relying on the exit code or the agent’s explanation. Look for changes that suppress checks, edit generated files that will be overwritten, alter toolchain or environment settings, or otherwise make the test pass without correcting the underlying problem. Ask whether the change belongs in source, generated output, build configuration, or test setup—and whether it will survive the project’s normal workflow.
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Lee says she read all 17 notes associated with passing builds and found that they matched the diffs. She also acknowledges that this was one person’s review and that the notes were short. A matching note is useful context, not a substitute for examining the actual change.
Enforce boundaries outside the prompt
A written instruction is not a security boundary. If an agent must not access the network or particular files, enforce those limits in the platform or sandbox rather than trusting the agent to comply. The evaluator should record restrictions and distinguish an attempted or blocked operation from an ordinary build failure.
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Check the judge as carefully as the agent
An evaluator can report a false failure if its environment is wrong or its restrictions prevent a legitimate build step. In an earlier run, Lee attributed six of 39 apparent build failures to her sandbox. After installing the compiler versions pinned by those projects, all six built and three passed their tests; Lee reported corrected totals of 113 builds out of 164. This is a warning about that evaluation setup, not a general estimate of sandbox error.
Network restrictions created another problem in the later experiment: they blocked hosts used by contract compilers and a proving step, producing artificial failures. Lee added a preflight run behind the network fence and an outcome labeled “fence blocked.” A useful judge needs to detect when its own controls obstruct the task instead of misclassifying that obstruction as a broken project.
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What the experiment does—and does not—show about model choice
The model comparison in Lee’s account is too small and confounded to establish which model is generally better at repairing builds. The first seven machines used a larger model and the next 26 used a smaller one, so model choice was entangled with the order and batch of machines.
In a later rerun of the first seven, Lee reported that the larger model fixed four of four real bugs, while the smaller model fixed one of four. The smaller model’s one success edited generated types that would be overwritten. Lee explicitly cautioned that seven machines provide an observation, not a rate. The result does not establish a reliable performance difference.
What a responsible workflow should conclude
A build-repair agent can reduce a specific failure, but its output should remain a proposed change until the checks and review are complete. Lee says none of the changes in this experiment were pushed. Her concise principle is: “The agent never grades its own homework.” A passing command is one piece of evidence; independent execution, a credible diff, a valid environment, and human acceptance determine whether the project is ready to accept the fix.
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