At Ramp, engineers are leveraging Codex with GPT-5.5 to accelerate code reviews and build internal agentic tooling. According to Austin Ray, who leads Ramp's AI Developer Experience team, the tool has become a mandatory part of many code review flows, allowing teams to receive substantive pull request feedback in minutes instead of hours. Ray notes that Codex stands out from other tools due to its ability to deeply reason against large codebases, providing a level of thoroughness that exceeds typical human review timelines. In addition to code reviews, Ray is using Codex to support the development of On-Call Assistant, an agentic tool designed to handle complex business logic, domain knowledge, and high-stress incident investigations. By relying on the reasoning capabilities of Codex, the On-Call Assistant has become significantly faster to build. Ray evaluates developer tools based on whether they genuinely change how code is shipped rather than just serving as demos. He recommends that engineering leaders focus on hands-on experiences, guide engineers through initial sessions to build trust, and invest in direct feedback loops with vendors. Ultimately, Ray believes that tools like Codex are shifting the role of software engineers toward becoming orchestrators who know how to direct AI systems effectively.
How Ramp engineers accelerate code review with Codex
Engineers at Ramp are utilizing Codex with GPT-5.5 to accelerate code reviews and develop internal agentic tooling like the On-Call Assistant. According to AI DevEx lead Austin Ray, Codex reduces manual effort and provides substantive pull request feedback in minutes instead of hours. The tool's deep reasoning capabilities help teams manage complex business logic and increase software development velocity.
Why it matters
This implementation demonstrates how advanced AI reasoning models can integrate into enterprise developer workflows to reduce manual overhead and improve code review velocity at scale.
Topic dossiers OpenAI