Why coding agents moved the review bottleneck from writing to reading

Updated: 2026-10-02.

Coding agents can make producing a change faster. Reading the change, checking its assumptions, and deciding whether it should merge still take human attention. When output grows faster than review capacity, the queue moves from implementation to review.

This is a workflow argument, not a measured claim that every team has the same bottleneck. Some teams are constrained by testing, product decisions, or deployment. Start with your own review queue and defect history.

What useful review needs to read

A review should check reachable paths, callers, contracts, security boundaries, and regression evidence. Style alone does not establish correctness. A reviewer also needs to say when the available diff or repository search is insufficient.

Local unpublished changes and hosted PRs provide different context. Code Terrier's terminal local mode receives an uploaded diff; its repository-backed execution can explore a confined checkout. Neither bounded search nor a fluent explanation proves exhaustive coverage.

How Code Terrier helps

Code Terrier reports categorized findings on changed lines, consolidates duplicates when multiple reviewers are configured, and applies verification before publication. The architecture supports up to three reviewers; the current shared pool has one model. Separate verification context reduces reliance on the original reviewer's reasoning, without guaranteeing correctness.

In auto mode, a later push starts a new review with bounded prior discussion. On-creation and manual modes keep that trigger under the team's control. Pi Durable persists the execution and resumes after restart only when the original credentials are renewed.

The bot never approves or merges. A person validates a finding, chooses a fix or disposition, and retains the final decision. A clean review is a completed report with no findings, not evidence that the reviewer examined every possible failure.

Install Code Terrier if an additional read-only review fits your process. Measure whether it saves attention on your own changes rather than assuming that more models or more comments make review better.