Pull requests replace issue tracking as AI-driven software shifts the bottleneck downstream

CodeRabbit, an AI-powered code review platform, unveiled its Agentic Change Management control layer, a governance system designed to help engineering teams oversee software changes produced by both human developers and AI agents. The announcement underscores a deeper transformation in how teams manage the software development lifecycle as coding becomes abundant but human judgment remains scarce.

The traditional SDLC breaks under AI acceleration#

Traditional issue trackers—including platforms like Jira, GitHub Issues, and Linear—were designed for a different era. They assumed product managers and engineering leaders would have time to carefully plan capacity, curate backlogs, and deliberate before assigning work to developers. That model no longer fits.

According to CodeRabbit CEO Harjot Gill, "issue tracking is dead" in an AI-native world. As coding agents can now generate and submit changes continuously, code increasingly exists before teams have aligned on priorities, assessed feasibility, or decided whether it's worth shipping. This creates a paradox: the marginal cost of producing code approaches zero, but human alignment and decision-making remain expensive. The pull request—once a mechanism for peer review—has become the crucial checkpoint where teams triage, prioritize, and govern incoming changes.

From code review to change governance#

The pull request now serves as what CodeRabbit calls the auditable decision point—the moment where teams evaluate whether a change meets quality standards, assess its risk, determine if it needs human attention, and decide whether to accept it. This marks a fundamental shift in how code arrives and gets managed. Rather than flowing from issue tracker to developer to pull request, code now originates from agents, prior systems, and non-technical personnel, creating a new triage layer.

At this higher level of abstraction, human reviewers focus on intent, architecture, behavior, and risk rather than inspecting individual lines of code. Gill explains: "When code is abundant, human judgment becomes the scarce resource." Review cycles can also incorporate automated fix-and-re-review loops, where agents address findings before human eyes review the change.

How the control layer works#

CodeRabbit's three-part system addresses this shift: