Introducing ambient agents
Most AI apps today follow a familiar chat pattern ("chat" UX). Though easy to implement, they create unnecessary interaction overhead, limit the ability of us humans to scale ourselves, and fail to use the full potential of LLMs. Over the past six months, we've been exploring a different approach at LangChain: agents that respond to ambient signals and demand user input only when they detect important opportunities or require feedback. Rather than forcing users into new chat windows, these agents help save your attention for when it matters most. We built LangGraph to make these patterns easy to implement. Today we're sharing our first reference implementation: an email assistant that demonstrates key ambient agent patterns. Over the next few days, we'll release additional examples and tooling to help you build your own ambient workflows.
When using ChatGPT (or any other chatbot), they rely on you to initiate the conversation. The agent is kicked off by the human sending a message.
