Agents Need Feedback Loops, Not Perfect Prompts

Buzz, a specialized agent developed by Warp, is designed to handle the multitude of mentions the company receives across social media platforms by automating the process of monitoring, drafting, and suggesting replies. Initially programmed with a rigid set of rules, Buzz evolved to operate on principles, which allowed it to adapt better to varying contexts and sound more authentically human. The agent continuously learns from human feedback, with team members providing insights through simple interactions like emoji reactions on Slack. This feedback loop helps refine Buzz's skills, which are treated like code with version control to ensure quality. As Buzz processes thousands of mentions monthly, it enables the team to focus on significant interactions that require human judgement, thus optimizing productivity without increasing team size. By embedding this learning process within the team's existing workflows, Warp enhances both the agent's effectiveness and the team's decision-making capabilities, creating a compounding effect on judgement and engagement.