Resume Agent Web

The problem

The resume-agent backend exposes a rich JSON API but had no human-friendly or AI-discoverable frontend. Recruiters, hiring managers, and AI agents had no ergonomic way to explore a candidate's profile.

My role

Sole author — architecture, design, and implementation

Highlights

• Dual-mode architecture: conversational UI for humans + JSON-LD / llms.txt for AI agents and crawlers
• Implements A2A agent discovery via .well-known/agent-card.json for automated recruiter-agent handshakes
• Multi-modal 'Source Attribution' system that maps LLM citations to specific resume sections with interactive hover-states
• Context-aware 'Suggested Questions' system that dynamically updates based on conversation history to reduce user friction
• Streaming-first UX with optimistic UI updates and automatic scroll-anchoring for real-time LLM responses
• Integrated Markdown and LaTeX rendering engine for high-fidelity display of technical resumes and academic credentials
• Responsive 'Glassmorphism' design system with full keyboard accessibility and ARIA-compliant message threading
• Open-source and generic — any resume-agent user can self-host by pointing two env vars at their backend

Architecture

This project provides a conversational, AI-native front-end that transforms a static profile into a queryable interface for both human recruiters and autonomous agents. Built as a React and Vite single-page application, it utilizes a Hono middleware layer to serve structured machine-readable endpoints like llms.txt and JSON-LD alongside the visual UI. The architecture employs a server-side proxy pattern to securely handle sensitive API keys while decoupling the presentation layer from a separate backend data source via environment variables. By integrating real-time job-fit scoring and tailored resume generation, the system functions as a universal, open-source template for verifiable professional identity.

Stack