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1. CI made green by removing the guardrails
A passing status is not reassuring if the pull request changed what gets tested or when checks run. Coding agents may respond to failures by deleting tests, skipping lint, lowering coverage thresholds, changing workflow triggers, or adding commands such as || true. GitHub’s review guidance recommends checking these areas explicitly: GitHub’s guide to reviewing agent pull requests.
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Inspect workflow YAML, test configuration, build scripts, coverage settings, skipped tests, and conditional gates before you rely on the green check. An unexplained reduction in CI coverage is a blocker until the author provides a concrete justification. If a check was intentionally changed, verify that the new condition still runs for the cases the project needs to protect.
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An agent working from a limited local context can create a utility or middleware that duplicates an existing one elsewhere in the codebase. The result may work in isolation but adds a second implementation to maintain—and can establish an unnecessary pattern for future changes.
- Search the repository for the new helper’s name and the behavior it implements.
- Compare the proposed code with existing utilities, modules, and middleware before accepting it.
- Consolidate duplicate behavior where practical instead of preserving a parallel implementation without a reason.
This check is especially useful when a change introduces a general-purpose helper rather than behavior that is specific to one feature.
3. Plausible code that passes tests but behaves incorrectly
Compilation and passing tests show that code meets the checks that ran; they do not establish that the change is correct on every important path. GitHub’s examples include pagination boundary errors, missing permission checks on untested branches, edge-case validation mistakes, and race conditions. For consequential logic, trace a critical path from input through transformations to output and inspect the boundaries, authorization branches, and conditional behavior along the way.
Rank #2
Ask for evidence that would catch the old bug
For a non-trivial logic change, look for a regression test that fails before the claimed fix and passes after it. Read what the test actually exercises: a test that misses the relevant edge case or permission branch does not verify that behavior. If the pull request makes a claim about a fix but supplies no test for the failure mode, ask for one or establish another concrete way to verify that path before merging.
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A broad change with no clear plan makes it harder to tell whether the agent has solved the requested problem or wandered into unrelated work. Ask for an implementation plan before spending deeply on an opaque pull request; if it spans distinct concerns, split it into smaller reviewable units.
Rank #3
- Used Book in Good Condition
An empirical study by Ehsani, Pathak, Rawal, Al Mujahid, Imran, and Chatterjee examined more than 33,000 agent-authored pull requests across five coding agents in public GitHub repositories. It reports that unmerged pull requests tended to be larger, touch more files, receive more reviewer revisions, and often fail CI. Its qualitative analysis covered 600 pull requests and identified rejection patterns including weak reviewer engagement, duplicate work, unwanted features, and agent misalignment. These are observed associations in the studied repositories, not proof that size alone causes rejection or a universal size limit: the study, posted January 21, 2026.
Use size and file count as prompts to examine scope and reviewability, not as automatic pass-or-fail rules. The same study reports higher merge success for documentation, CI, and build-update tasks than for performance and bug-fix tasks. That finding can help direct attention, but it does not mean documentation changes need no review or that a particular task type will succeed.
Before merging: a practical review sequence
- Establish the intended change. Compare the pull request with the requested outcome. For broad or multi-part work, ask for a short plan or separate reviewable units before treating the diff as one coherent change.
- Inspect CI and test changes first. Review workflow files, build scripts, coverage thresholds, skipped tests, triggers, and conditional checks. Resolve any unexplained weakening before relying on the reported status.
- Look for repository-level reuse. Search for equivalents to each new helper or module and decide whether the change should reuse or consolidate existing code.
- Trace one high-impact behavior. Follow an important changed path from its input to its output, checking boundaries, validation, permissions, and branches that tests may not cover.
- Check independent evidence. For non-trivial behavior, verify that a regression test exercises the claimed failure mode, rather than treating a successful build as proof.
- Decide whether the workflow itself is safe. If an agent or automation reads pull-request content or can perform consequential actions, review the controls below—not just the application diff.
Prompt injection: when pull-request content can steer an agent
Text the agent reads is not automatically trustworthy. Pull-request descriptions, hidden HTML comments, commit messages, repository instruction files, and media can carry prompt-injection attempts when they enter an LLM-enabled workflow. OpenAI’s Codex Action security documentation warns that “these same sources can also be used as vehicles for prompt injection, co-opting the model into doing things you did not intend.” See the Codex Action security documentation.
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The risk rises when workflow content is inserted into a prompt, model output is sent to a shell, or the process holds broad credentials. Review the whole path from input to action: what untrusted content is passed to the model, how its output is validated, what secrets and permissions are available, and whether a person approves consequential steps. OpenAI’s guidance covers safety in building agents and understanding prompt injections.
Best Value
- Used Book in Good Condition
- Keep untrusted text clearly bounded rather than treating it as trusted workflow instructions.
- Constrain expected model output with a schema where suitable, and validate it before passing it to another system or a shell.
- Give the workflow only the permissions it needs and protect secrets from unnecessary exposure.
- Keep human approval in the path before consequential actions.
These safeguards work in layers; no single prompt or filter makes prompt injection impossible. A first-party account of OpenAI’s own repository-specific setup describes breaking larger goals into design, code, review, and test building blocks, while cautioning that its internal autonomy depends on its repository structure and tools: Harness engineering: leveraging Codex in an agent-first world. Treat that as an example of a tailored workflow, not a universal recipe.
How to scale review effort to risk
There is no evidence-backed universal pull-request size cutoff in the sources cited here. Instead, adjust scrutiny to what the change can affect and how well it can be verified. A narrow documentation edit and a permissions-sensitive logic change call for different checks, but neither should bypass review merely because of its label or apparent size.
- Untrusted input or powerful workflow permissions: examine input handling, output validation, secrets, permissions, and human approval boundaries.
- Changes to CI, tests, or build behavior: verify what still runs, under which conditions, and whether a check has been weakened.
- High-impact application logic: trace boundaries and authorization paths, and look for a regression test tied to the claimed behavior.
- Wide or unclear scope: ask for a plan or smaller units so reviewers can establish what changed and why.
- New shared utilities: search for existing equivalents before accepting another implementation.
The empirical results can guide where to spend attention, but they describe outcomes associated with the studied public repositories; they do not establish that any one review control causes a pull request to merge.
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