How do I keep up with hundreds of small AI-agent PRs?

A queue of hundreds of small pull requests from an AI agent is a triage problem before it is a reading problem: decide what needs a careful human read and what does not, before you open a single diff. DiffGuardian speeds that triage by splitting each pull request into the features it ships and giving you the quickest way into each one: read it, hear it, or see what it touches. For many agents working at once, see How to keep up with coding agents working in parallel.

#A daily triage method, without any tool

This works whether the queue came from one agent or five, and it does not depend on DiffGuardian or any other tool.

1. Pull the whole queue into one list before reading anything. Reviewing PRs in the order they arrived means the riskiest one might be last. Look at the whole set first.
2. Sort by risk category, not by size. A one-line change to an auth check or a migration is riskier than a three-hundred-line change to a test fixture. Read the categories that touch authorization, payments, migrations, config, and public API surface first, regardless of how small they look.
3. Batch the near-duplicates. Agents frequently open several PRs that make the same mechanical change across different files — a lint fix, a dependency bump, a rename. Diff two of them against each other; if they are the same shape, review the pattern once and apply the same verdict to the rest instead of re-reading each one in full.
4. Check the description against the diff before trusting either. An agent's PR description states what it intended to do. Confirm the diff actually does that and nothing else — an agent that was asked to fix a bug and also touched an unrelated file is the case this step is for.
5. Reserve full attention for the categories from step 2, and a lighter pass for everything else. Not every PR earns the same amount of your time; deciding that upfront is what keeps the queue moving.
6. Leave anything you merged on a lighter pass somewhere you will see it again, in case a later PR interacts with it in a way that was not visible in isolation.

#How DiffGuardian speeds each step

• Step 1 and 2 — risk within each PR, and a signal across the queue. DiffGuardian reads a pull request against the whole repository it changes, then splits the change into the discrete features it actually ships and orders those features by risk, so the riskiest part of any single PR is what you see first. Across the queue, a higher-risk PR is flagged with an advisory indicator, so you can tell at a glance which ones need the careful pass from step 2. See Review a big PR feature by feature.
• Step 2 and 6 — what a change actually touches. DiffGuardian's blast-radius view builds a dependency graph from the diff alone: the changed files and the modules they import. With a local checkout of the repository, it also adds the upstream callers that flow into an edited file, drawn solid, plus dashed API and channel edges — so with a checkout you can also see what reaches back into a small-looking change before you decide it is safe to skim. See See what a pull request actually affects.
• A hands-free option for a long queue. Talk to your pull request (Pro) lets you ask what it does and hear the walkthrough instead of reading it line by line, with the code it is explaining highlighted as it talks. Voice runs on your own OpenAI or ElevenLabs key, or your system's built-in voice.

DiffGuardian is a desktop app for macOS, Windows and Linux, and works with pull requests on GitHub, GitLab and Bitbucket.

#Limits

• DiffGuardian orders and explains the changes; it does not merge or approve anything on its own. The decision stays with the reviewer.
• Voice is a Pro feature. On Basic, the feature split runs on a local model — Ollama, LM Studio, llama.cpp or vLLM; using your own cloud-provider key needs Pro. The blast-radius view needs no AI and works on every plan.
• The blast-radius view drawn from the diff alone is heuristic, not AI; the extra edges from a local checkout need that checkout to be present.