5 Rules for Building AI Agents That Work in Production | Nan Yu & Jacob Shumway
A behind-the-scenes look at building a production AI agent end to end, including how to give it tools to find the context it needs and use evals to measure output quality.
Nan Yu and Jacob Shumway discuss the development of Linear Agent, an AI that has changed how a $1.25B company operates by automating workflows. Key advice includes mapping actual workflows, giving agents tools to find context rather than pre-loading it, and starting with the largest models during prototyping. The episode offers a concrete, end-to-end example of building a production AI agent, covering its journey from initial memo to launch.
- Linear Agent has transformed how a $1.25B company gets work done.
- Building an AI agent involves stages from initial memo to launch, including providing tools for context and designing reliability evals.
- To build a good agent, map the actual workflow: identify start points, systems holding context, actions, completion criteria, and human review points.
- Integrate agents with existing platforms like Slack as the entry point, rather than forcing users into separate chatbots.
- Give agents tools to find context instead of stuffing prompts with information; start with minimal instructions and let the agent retrieve necessary data.
- During prototyping, use the biggest model until the task is accomplished.
- Oceans Talent offers AI-fluent operators who can build and maintain AI workflows at a reduced cost compared to US hires.
https://bender.layer3.press/articles/93a28cd3-1e12-4efe-8716-8955ee6c7f60
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