# OpenAI Codex 5.2 High CLI Test on a Real Fleet Dashboard Source: https://www.youtube.com/watch?v=rIf09FpFHUs Recap page: https://rapidrecap.app/video/rIf09FpFHUs Generated: 2025-12-19T22:33:25.235+00:00 --- ## Quick Overview The OpenAI Codex model version 5.2 running in 'high' reasoning effort successfully generated a complex fleet management dashboard application from a detailed project plan and OpenAPI specification, demonstrating significant capability in translating complex project documentation into functional code and application structure, although some minor issues like slow loading times and console errors were encountered during the demonstration. **Key Points:** - Codex 5.2 (High reasoning) successfully scaffolded a full-stack React/TypeScript fleet management dashboard based on a provided project plan and OpenAPI spec. - The agent completed all major planned phases, including API inspection, scaffolding, implementing core features (Fleet Overview, Deliveries, Trucks, Analytics), and handling deployment configurations. - The resulting dashboard displayed live data, KPIs (Total Trucks: 50, Active: 43, Open Deliveries: 297, Completion Rate: 98%), and working filtering/routing. - During the demonstration, the agent encountered an error on the Analytics page related to 'Rendered more hooks than during the previous render,' which was fixed via code update. - The agent noted that the 'cancelled' status appearing in console logs during data fetching was due to query layer aborting fetches on cache updates. - The final application successfully rendered data across all main views (Overview, Deliveries, Trucks, Analytics) using the production API endpoint. - The entire process, from initial plan to functional application, took approximately 18 minutes and 40 seconds of continuous agent work. ![Screenshot at 00:04: The video displays the initial setup with the Agent CLI running, showing the project structure, the project plan markdown file detailing features, and the agent actively processing commands in the terminal.](https://ss.rapidrecap.app/screens/rIf09FpFHUs/00-00-04.png) **Context:** The video demonstrates an advanced test of the OpenAI Codex model, specifically version 5.2 configured for 'high' reasoning effort, using the Agent CLI tool. The goal was to have the AI agent autonomously build a complex, full-stack Fleet Management Dashboard application solely by referencing a provided project plan in Markdown and an OpenAPI specification file (fleet-api-spec.yaml). The presenter, Sterling Chin, monitored the agent's progress through various development phases, including scaffolding, API integration, and feature implementation, while troubleshooting minor errors encountered along the way. ## Detailed Analysis The demonstration involved testing OpenAI Codex 5.2 in 'high' reasoning mode via the Agent CLI to build a Fleet Management Dashboard. The agent successfully interpreted the project plan and OpenAPI specification to scaffold the application structure, including setting up routing, API layers, and individual pages (Fleet Overview, Deliveries, Trucks, Analytics). The agent implemented core features, such as displaying KPIs on the overview page (50 total trucks, 43 active, 98% completion rate) and enabling interactive data filtering on the Deliveries and Trucks pages. During the process, the agent encountered and fixed an error related to 'Rendered more hooks' on the Analytics page by updating the state query cache logic. The agent also observed and debugged network issues, noting that the 'cancelled' status messages in the console were expected behavior related to query layer abortion during cache updates. By the end of the test, the application was fully functional, displaying real-time data (including a live map scatter plot) pulled from the production API endpoint. ### Project Setup & Initial Plan - Agent CLI initialized with Codex 5.2 High model - Project involved building a full-stack React/TypeScript Fleet Ops Dashboard - Files included project_plan.md and fleet-api-spec.yaml ### Core Feature Implementation - Agent successfully scaffolded app structure, implemented routing, and built out four main pages (Overview, Deliveries, Trucks, Analytics) based on the plan. ### Fleet Overview Metrics - Dashboard displayed key KPIs: 50 Total Trucks, 43 Active, 297 Open Deliveries, 98% Completion Rate, and a functional Live Map showing GPS scatter. ### Troubleshooting & Refinement - Agent encountered and fixed a 'Rendered more hooks' error on AnalyticsPage.tsx - Addressed 'cancelled' fetch requests noted in the console by updating query cache logic. ### Data Verification - Navigating to Deliveries and Trucks showed correct data loading, filtering, and sorting functionality working as specified in the plan. ### Conclusion - The entire complex build was completed successfully in approximately 18 minutes and 40 seconds of agent time, confirming Codex 5.2's strong capabilities in complex project execution. ![Screenshot at 00:01: The terminal session showing the Agent CLI initialized with OpenAI Codex v0.71.0 and model set to gpt-5.2 high.](https://ss.rapidrecap.app/screens/rIf09FpFHUs/00-00-01.png) ![Screenshot at 00:12: The agent interacting with the project plan \(project\_plan.md\) and OpenAPI specification \(fleet-api-spec.yaml\) files.](https://ss.rapidrecap.app/screens/rIf09FpFHUs/00-00-12.png) ![Screenshot at 00:25: The fully rendered Fleet Dashboard UI showing KPIs and the live map section, indicating the agent successfully built the application frontend.](https://ss.rapidrecap.app/screens/rIf09FpFHUs/00-00-25.png) ![Screenshot at 02:01: The terminal output showing the agent successfully changing the model from gpt-5.2 high to gpt-5.2 high \(a confirmation step\) and then proceeding with task execution.](https://ss.rapidrecap.app/screens/rIf09FpFHUs/00-02-01.png) ![Screenshot at 14:47: The Chrome Developer Tools showing the Network tab filled with script and fetch requests, indicating the application successfully loaded all necessary assets and data.](https://ss.rapidrecap.app/screens/rIf09FpFHUs/00-14-47.png)