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Harness Rebuilds Code Repository and Launches AI Code Review to Handle Agent-Scale Pull Request Volume

Harness launched an agent-ready code repository and an AI Code Review product built to handle thousands of pull requests per second as coding agents multiply PR volume.

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Overview

Software delivery platform Harness has launched two new products aimed at software teams whose pull-request volume is being reshaped by AI coding agents: an Agent-Ready Harness Code Repository built to absorb agent-scale commit traffic, and an AI Code Review tool designed to triage that traffic before it merges. SiliconANGLE reports that “software delivery platform provider Harness Inc. today announced the launch of Agent-Ready Harness Code Repository and AI Code Review, aimed at developer teams adopting artificial intelligence coding agents at an ever-increasing pace.”

What We Know

  • Harness describes the launch as introducing “two capabilities built to close that gap: Agent-Ready Harness Code Repository, a source code management (SCM) system designed to handle agent-generated code at scale, and AI Code Review, which reviews that code faster before it merges,” according to the company’s own announcement. Both are now available as part of Harness’ Software Delivery Agent.
  • SiliconANGLE frames the launch as Harness rebuilding its permission and code-keeping layer: “The permission and code-keeping systems designed to handle hours and days of work can no longer keep up with this lifecycle. Harness said it’s rebuilding that layer.” The repository itself is “scale tested to handle thousands of pull requests and commits per second,” Harness says, a throughput claim SiliconANGLE independently describes as source control “scale-tested to handle thousands of pull requests and commits opened at once, meaning a team of hundreds or thousands of agents working all day can do so without blocking.”
  • Harness co-founder and Chief Executive Jyoti Bansal is quoted by SiliconANGLE saying: “Software delivery is going through its biggest shift since the move to the cloud, and the systems we all built our workflows around were designed for a different scale and a different kind of user.” Bansal added: “The entire SDLC has to become autonomous.”
  • On permissions, Harness explains that “agents inherit permissions from the developers who trigger them. Developers can further define what an agent may access or merge, just as they would scope a new engineer, down to a specific repository, branch, or environment.” SiliconANGLE corroborates that each agent “receives its own permissions by inheriting from the human that triggers it, down to the specific repository, branch, project or environment.”
  • Harness says it “tailored the entire system to use Model Context Protocol and command-line interfaces,” per SiliconANGLE. The Harness blog adds that its CLI “supports structured commands such as creating repositories, searching for PRs by author email, or pulling all open PRs into a single cross-repository inbox.”
  • The new AI Code Review tool “looks at a pull request the way a tech lead would,” running “customizable AI checks against the change,” grouping “the diff logically rather than by file,” and writing “feedback grounded in the risk associated with the introduced changes,” Harness says. With a feature called Diff Grouping by Risks, “developers can see the riskiest changes first,” and required checks that fail “can’t be squashed and merged.” Harness also notes that “AI Code Review also works on GitHub repositories today.”
  • The review tool draws on what Harness calls an SDLC Knowledge Graph, evaluating changes against an organization’s own incident history. In one example Harness describes, the tool “caught a new index migration and flagged it as high-risk, surfacing a prior incident where an unindexed CREATE INDEX statement had locked a production table for 14 minutes, and reminding the author that the RCA called for CREATE INDEX CONCURRENTLY going forward,” according to the company.
  • Harness cites Gentera as an existing customer of the Code Repository product: “Organizations like Gentera use Harness Code Repository to standardize DevOps across a regulated banking environment, reducing permission changes from weeks to minutes and improving delivery speed by 4x while halving the cognitive load of switching tools.”
  • On its own dogfooding, Harness says: “our engineering team has been using Code Repository and AI Code Review on our own pipelines. With hundreds of developers contributing code, we’ve seen over 10,000 hours of savings over the last month by using AI Code Reviews.” SiliconANGLE reports the same internal usage independently: “Harness has used both capabilities internally for months. From early testing, teams saved an estimated 10,000 hours over the last month.”
  • Harness Code ships with “50 GB per account on the Free tier, and 500 GB per account on paid plans (covering both Git and LFS storage),” and supports importing “a single repository or an entire GitHub org, GitLab group, Bitbucket workspace, or Azure DevOps project directly from the UI,” per Harness.

What We Don’t Know

Harness has not published independent, third-party benchmark results verifying the “thousands of pull requests and commits per second” throughput claim or the 10,000-hours internal savings figure beyond its own reporting. Pricing details for AI Code Review itself, and how it is metered separately from existing Harness plans, were not specified in either source.

Analysis

The launch reflects a broader shift the software-delivery industry has been grappling with as coding agents generate a growing share of pull requests: existing code-review and source-control tooling was largely designed around a fixed number of human contributors working standard hours, not agents that can open large volumes of pull requests continuously. Harness’s pitch — that the repository and the review layer must evolve together, since fixing one without the other simply relocates the bottleneck — positions the company alongside other software-delivery vendors racing to adapt code review and repository infrastructure to agent-generated volume.