I Built an AI Coding Agent That Researches the Web Before It Writes Code

Описание и применение

Автор создал агент, который сначала исследует веб‑документацию, а затем генерирует код, улучшая точность и актуальность решений.

Задача

AI coding agents often generate code based on internal knowledge, which can miss recent API changes or version differences, leading to incorrect or incomplete implementations.

Результат

The agent successfully produced a research-backed implementation plan and TypeScript source files for an HTTP client, demonstrating that pre‑research can surface relevant documentation and improve code reliability.

Рабочий процесс

• Initialize a TypeScript project and install dependencies
• Configure npm scripts, .gitignore, and environment variables
• Obtain a Context.dev API key and add it to .env
• Integrate Context.dev via OpenCode, creating a research module and API wrapper
• Run type checks, automated tests, and a dry‑run to validate the research pipeline
• Execute the agent on a live coding task to generate a TypeScript HTTP client with retry and timeout logic

Доказательства

I wanted to experiment with a different approach: research first, plan second, generate code third.

Источник

The agent researched HTTP client implementations, retry strategies, and timeout handling. It then produced a structured implementation plan and proposed TypeScript source files.

Источник