Ai-Agents

An LLM agent may have access to a calculator, a browser, a code runner, GitHub, Slack, Jira, Google Drive, a database, and hundreds of internal APIs. Giving the agent more capabilities sounds useful. But there is a practical problem: every tool normally comes with a name, description, and input schema. If I place all of those definitions in every model request, the model has to read a large API catalog before it can answer a simple question. ...

Coding agents are useful when they can read code, edit files, run tests, and explain errors. But web development has a problem that does not fit neatly inside the file system: the real bug often lives in the browser. A React component may look fine in code but overflow on mobile. An API call may fail only after a specific login state. A button may be present in the DOM but not clickable. A performance issue may come from layout shifts, long tasks, font loading, image decoding, or network waterfalls. A console error may point to bundled JavaScript that needs source maps to be useful. ...