Cato Networks Showcases Internal Agentic AI Tool for Code Review Efficiency

According to a recent LinkedIn post from Cato Networks, the company’s R&D organization has implemented an in-house “agentic AI” tool to support pull request (PR) code reviews. The post describes this as a self-evolving PR review agent designed to operate under real-world constraints with a focus on measurable return on investment.

The post highlights reported performance metrics, including a 70% catch rate on PRs linked to incidents and about 7,000 high and critical findings identified per month. It also indicates that roughly 70% of those findings are approved and resolved by engineers, suggesting that the output is materially actionable rather than noise.

According to the post, the cost structure appears predictable at roughly $10 per unique user, implying a relatively low per-developer expense for an automated quality gate. The description emphasizes continuous learning from developer feedback and long-term memory, positioning the system as an always-on layer that may reduce engineering risk and review time.

For investors, these details suggest an internal focus on leveraging AI to increase software quality and engineering efficiency, which could support scalability without proportional headcount growth. If such tooling proves effective and is generalized across Cato Networks’ development efforts, it may contribute to better reliability in the company’s networking and security offerings and potentially enhance its competitive position in AI-driven DevOps practices.

The initiative also signals broader engagement with cutting-edge AI methodologies, which could strengthen the firm’s perception as an innovator in secure networking. While the post does not quantify direct revenue impact, improved code quality and faster release cycles may have downstream benefits for customer satisfaction, retention, and long-term operating margins.