Systems Architecture for Agentic AI in Next.js

说明与用途

基于事件的架构将代理执行与展示解耦，降低 UI 延迟并实现水平扩展。

问题

Agentic applications built on monolithic request-response patterns suffer from slow, brittle behavior due to multi-step reasoning loops, API gateway timeouts, and frontend sluggishness.

结果

The platform achieved sub‑50 ms perceived UI latency, eliminated serverless runtime limits, and scaled horizontally to handle thousands of concurrent multi‑step reasoning sessions.

工作流

• 1. User submits a task via Next.js frontend. 2. Serverless function queues the task in a message broker and returns 202 Accepted. 3. Background workers process each agent step (plan, act, reflect) and emit AgentStep objects. 4. WebSocket channel streams AgentStep updates to the browser. 5. Frontend renders optimistic UI updates, showing the agent’s thought process in real time.

证据

We introduced a message broker. This allows us to queue agent tasks immediately. Once a task is submitted, the serverless function can return a 202 Accepted status, freeing up resources while the heavy lifting happens in the background.

来源

Our perceived UI latency dropped to sub‑50ms because the frontend no longer waited for the entire agent workflow to finish before showing the first "thought."

来源