A LinkedIn post from Cato Networks describes how its R&D organization is applying agentic AI to automate pull-request (PR) code reviews. The post outlines an internally built, self-evolving PR review agent designed to operate as an always-on quality gate that learns from developer feedback and long-term memory.
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According to the shared metrics, the AI system reportedly achieves a 70% catch rate on incident-linked PR evaluations and surfaces roughly 7,000 high and critical findings per month. The post also notes that about 70% of these findings are approved and resolved by engineers, with an estimated predictable cost of around $10 per unique user.
For investors, this suggests Cato Networks is investing in AI-driven productivity and quality improvements in its development pipeline, which could enhance product reliability and speed of delivery over time. If sustained, such internal efficiency gains may support better operating leverage, reduce incident-related costs, and strengthen the company’s competitive position in secure networking and cloud-based security markets.
More broadly, the move illustrates how agentic AI can be deployed with measurable ROI in software engineering workflows, an area of growing focus across the technology sector. Cato Networks’ emphasis on practical, constraint-aware AI implementation may make its technology stack more scalable while signaling to the market a strong internal capability in applied AI, a factor that could influence future valuation and partnership opportunities.