Integrating video and access doesn’t have to be a multi-day project. A new workflow makes the Brivo and Eagle Eye APIs “AI-Native”\u2014essentially making the documentation a tool for your own AI agents.<\/p>\n
We\u2019ve created an EEN-API agent skill that gives AI coding tools the context they need to work with the Eagle Eye API more effectively. Instead of manually digging through documentation, copying endpoint details, and correcting outdated assumptions, you can load your AI assistant with the skill and give it a focused task. The skill is designed to provide your agent with the required context for developing against our API in a structured way.<\/p>\n
To show you how much faster this is, we\u2019ve put together a few example prompts you can use to put our API through its paces. Try them yourself or customize them to fit your specific use case.<\/p>\n
\/een-api Create a small CLI tool that authenticates with the Eagle Eye API and lists the cameras available to my account. The tool should obtain the required client credentials from a `.env` file, walk a user through the authentication process, and print the camera names and IDs in a readable table.<\/p><\/blockquote>\n
\/een-api Create a node.js web application that authenticates with the Eagle Eye API and allows a user to search for a camera and view a live feed of what it sees. The application should obtain the required client credentials from a .env file.<\/p><\/blockquote>\n
The agent skill gives your AI assistant enough API context to generate the authentication flow, call the right endpoints, and produce a working piece of software that you can inspect, run, and adapt. Instead of starting from a blank file, you start from a working integration pattern.<\/p>\n
What will you build when the documentation handles itself?<\/h3>\n
We\u2019re curious to see how this changes your build speed. Grab our EEN-API agent skill and try the “AI-first” workflow in our repo:<\/p>\n