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Agent pull requests are already saturating review bandwidth
The volume is already staggering. GitHub Copilot code review has processed over 60 million reviews, growing 10x in less than a year. More than one in five code reviews on GitHub now involve an agent. That’s just the automated review pass. The pull request themselves are multiplying faster than reviewers can handle.
The traditional loop—request review, wait for code owner, merge—breaks down when one developer can kick off a dozen agent sessions before lunch. Throughput has scaled exponentially. Human review capacity hasn’t. The gap is widening.
You’re going to review agent pull requests. The question is whether you’ll catch what matters when you do.
Who (or what) actually wrote this pull request
Before you look at a single line of diff, you need a model for what you’re reviewing.
A coding agent is a productive, literal, pattern-following contributor with zero context about your incident history, your team’s edge case lore, or the operational constraints that don’t live in the repository. It will produce code that looks complete. But that “looks complete” failure mode is dangerous.
You’re the one who carries that context. That’s not a burden. It’s the actual job—the part of review that doesn’t get automated is judgment, and judgment requires context only you have.
Now, back to reviewers. The pull request lands in your queue. The author did their part. Here’s what to watch for.
Red flags to watch for
- 1. CI gaming: Agents fail CI. When they do, they have an obvious path to get tests passing: remove the tests, skip the lint step, add || true to test commands. Some agents take it.
- Any change that weakens CI is a blocker. Full stop. Before approving any agent pull request, check:
- Did coverage thresholds change?
- Were any tests removed, renamed, or marked as skipped?
- Did the workflow stop running on forks or pull requests?
- Are any CI steps now gated behind conditions they weren’t before?
Note: Yes to any of those means you need an explicit justification before you continue.
2. Code reuse blindness
This is the highest-ROI thing you can do as a reviewer. Agents look for prior art. They’ll find a pattern in the codebase and replicate it, often without checking whether a utility that already does the same thing exists somewhere else. The symptoms: new utility functions that duplicate existing ones with slightly different names, validation logic reimplemented in multiple places, middleware written from scratch that already lives in a shared module, helpers that are “almost the same” but with different names.
The agent’s local context doesn’t include the full picture of what exists across your repository. You do.
For every new helper or utility in an agent pull request, do a quick search. If you find an equivalent, don’t leave a comment. Require consolidation before merge. The cost of leaving duplicated logic is that agents will find it as prior art and replicate it further.
Pro tip: Require justification for adding new utilities in agent pull requests above a size threshold. This catches the duplication problem early.
3. Hallucinated correctness
The obvious hallucination (calling an API that doesn’t exist, referencing a variable out of scope) gets caught in CI. The dangerous one is subtler: code that compiles, passes every test, and is wrong.
- Off-by-one errors in pagination.
- Missing permission checks on a branch that never hits in tests.
- Validation that short-circuits under an edge case the agent never considered.
- Wrong behavior under a race condition that only surfaces at scale.
Trace it, don’t just scan it. Pick the most critical path in the diff. Follow it from input through every transform to output. Check boundary conditions (zero, max, empty), missing validation on external values, permission checks on every branch, and surprising conditional logic.
Note: Require a new test that fails on the pre-change behavior. If the agent can’t write a test that would have caught the bug it claims to fix, the fix is incomplete or the understanding is wrong.
4. Agentic ghosting
You leave a thorough review. You explain the issue, provide context, suggest a direction. The pull request goes quiet. Or the agent responds and misses the point entirely and runs in circles. You invest another round. Still nothing useful.
Larger pull requests with no structured plan correlate strongly with agent abandonment or misalignment. The larger and less scoped the pull request, the more likely you’re going to sink review time into something that goes nowhere.
Before you invest deep review on a large agent pull request check the pull request history. Has it been responsive in previous rounds? Does it have a clear implementation plan, or did the agent just start writing code?
If there’s no plan, request a breakdown before you write a single comment. Copy-paste version:
This pull request is too large for me to review without a clearer implementation plan. Can you break it into smaller scoped units, or add a summary of what each part does and why it’s structured this way? Happy to review after that.
5. Untrusted input in workflows
Prompt injection in CI agents is real and underappreciated. Here’s the pattern: an agent workflow reads content from a pull request body, an issue, or a commit message. That content gets interpolated into a prompt. The prompt goes to a model. The model output gets piped to a shell command. The whole thing runs with GITHUB_TOKEN permissions.
To ensure security:
- Is untrusted user input, pull request bodies, issue bodies, commit messages, being interpolated into prompts without sanitization?
- Is
GITHUB_TOKENwrite-scoped when it only needs read access? - Is model output being executed as shell commands without validation?
- Are secrets accessible to the agent step or being printed to logs?
To require before merge: least-privilege permissions in the workflow YAML (permissions: read-all is a reasonable default), sanitize and quote untrusted content before it touches a prompt, separate analysis from execution with a human approval gate for anything touching production, never eval model output.
| Time | Step | What to do |
|---|---|---|
| 1-2 min | Scan and classify | Narrow task (docs, CI, small change) or complex (multi-file, logic, performance, tests)? That classification sets your review depth for everything that follows. |
| 2-3 min | Check CI changes first | Before reading a single line of app code, look at anything touching .github/workflows, test configs, coverage settings, or build scripts. Flag anything that weakens CI. Stop sign check. |
| 3-5 min | Scan for new utilities | Search for new functions, helpers, or modules. For each one, do a quick repo search to check for duplicates. Flag anything that reinvents existing functionality. |
| 5-8 min | Trace one critical path | Pick the most important logic change. Trace it end-to-end: input → transforms → output. Check boundary conditions, permissions, unexpected branching. This is the step you can’t skip. |
| 8-9 min | Security boundaries | If this PULL REQUEST touches any workflow that calls an LLM or handles untrusted input, run through the security checklist above. |
| 9-10 min | Require evidence | For any non-trivial logic change, require a test that fails on the pre-change behavior. No rollback plan for risky changes? Ask for one. |
When to request a smaller pull request:
- The diff touches more than five unrelated files
- You can’t describe the purpose of the pull request in one sentence
- The agent has no implementation plan or the pull request body is empty
- CI is failing and the only changes in the diff are to test files
Let Copilot review it first
Use automated review for what it’s good at: catching the mechanical stuff before a human has to. Copilot code review flags style inconsistencies, obvious logic errors, missing error handling, and type mismatches. It handles the low-level scan. That frees you up.
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