Decagon’s Post

Today, we're publishing the AI Agent Engine! 🙌 It's a distillation of our learnings from many successful deployments of AI agents at enterprises. Ultimately, this is what's required for a successful implementation of AI agents. Of course, this is specific to our space (customer service & experience), but the themes will carry over to any vertical. 1. First, you have the "AI agent", defined as a software system that can autonomously do the work of a human agent, such as looking up data, taking actions, making complex decisions, and writing personalized responses. This is the holy grail that everyone wants to get to. 2. Around it, the rest of the engine is designed to reinforce the AI agent and allow it to continuously improve. This starts with a mechanism (i.e. "Routing") that determines when the conversation should be escalated to a human in the loop. This is key because it allows you to roll out your AI agent incrementally. 3. Next, you have the AI tooling for your human agent to use that automates away mundane tasks, like drafting an answer, finding relevant information, polishing tone, etc. We call this "Agent Assist", and it's akin to a copilot. 4. Then, the conversations all feed into a central data platform, our "Admin Dashboard", that allows the leaders of the team to use LLMs to analyze the conversations. This will surface themes, trends, and anomalies in the data easily. It'll also identify gaps in your knowledge, for example, and proactively tell you how to fix them. 5. Finally, you need a way for human staff to "QA" the conversations to constantly give feedback. We've built this directly into the product. These components form the AI Agent Engine, a helpful framework for thinking about AI implementations. The full post written by Bihan Jiang, Kaylee George, and Cynthia Chen is below! 👇