Project Overview
This platform project supports the configuration of knowledge and strategies for agent applications. My focus went beyond prototyping: I translated product requirements into clear specifications and used AI-assisted development to help deliver working features.
Pain Points & Opportunities
Configuration platforms must balance business expression, interaction efficiency, and engineering constraints. When requirements, rules, and interface behavior are not captured in a shared specification, product ideas are difficult to implement and validate consistently. This case discusses only the public product and AI Coding workflow, without disclosing business rules or internal systems.
Goals & Metrics
The goal was to establish a clear path from requirement definition to launch: produce reviewable product and interaction designs, capture essential behaviors in structured specifications, and use those specifications to guide implementation, integration, and acceptance.
Solution Design
I used a specification-first approach to define page goals, user actions, state changes, and exception boundaries before planning implementation with coding agents. AI Coding was not treated as one-shot prototype generation; it was an iterative process grounded in explicit specifications, verification, and correction.
Execution Plan
I owned requirement scoping and interaction design for related features and contributed to JavaScript frontend and Go backend implementation. I also worked with engineering and QA on integration testing, issue diagnosis, bug fixes, and deployment to keep the shipped behavior aligned with the product specification.
Results Review
The related features completed development, integration, and launch. The work established a specification-driven AI Coding workflow: clarify the problem and boundaries, generate and review the implementation, then validate it through testing. To protect business confidentiality, this page does not include internal metrics, rules, code, or system screenshots.
Iteration Reflection
The value of AI Coding is not limited to faster coding. It can shorten the distance between product judgment and functional validation. In practice, clear specifications, continuous result checks, and explicit handling of failure states determine whether generated code can enter a real delivery workflow.
Privacy note: This case presents only product responsibilities and AI Coding practices suitable for public sharing. It does not include internal business logic, performance metrics, code, or system materials.