How to Build Full-Stack Agent Applications

A simple agent can live in a command-line loop. A production one needs a full-stack architecture that separates the interface from the reasoning — for scalability, security, and a usable experience. The pattern below is technology-agnostic and splits into three communicating layers.

The three layers

Decoupling these makes the system easier to build, debug, and scale — and lets you replace any one layer without touching the others.

Layer 1: The front-end

The front-end’s job is to make the agent’s process legible. Beyond capturing the prompt, it must:

• Stream events in real time. Agents don’t return one answer; they emit a stream of thoughts, tool calls, and intermediate results. Rendering those live shows the user the agent is working.
• Manage state. Track conversation history and the task’s current status.
• Render rich output. Not just text — tables, charts, images, and interactive components produced by the agent’s tools.

The UI protocol problem

A hard question in full-stack agents is how the back-end tells the front-end what to render. Sending raw HTML or JavaScript from the model is insecure. A declarative UI protocol — such as Google’s A2UI (Agent-to-User Interface) — solves this: the agent emits a structured JSON payload describing the interface it wants (“a form with a date picker and a submit button”), and the front-end renders it with its own pre-approved native components. Secure, fast, visually consistent. See Agent-Driven Interfaces with A2UI.

Layer 2: The back-end API

The back-end is the secure bridge between the browser and the agent core. It handles:

• The endpoint — a single entry point for the front-end (REST, or a WebSocket for real-time streams).
• Security. The front-end must never hold LLM API keys or credentials; the back-end guards them.
• Sessions. Connect each user to the right agent instance and chat history.
• Orchestration. Pass requests to the agent core and stream its results back out.

Layer 3: The agent core

This is where the work happens. The core runs the reason-act-observe loop (see AI Agents Running Workflows), decomposing a goal into steps. Its components:

• Reasoning engine (LLM) — a frontier model (GPT, Claude, Gemini) that plans and decides.
• Orchestration — a framework like LangGraph or LlamaIndex, or custom code, that runs the loop.
• Tool belt — the APIs, functions, and clients the agent uses to act.
• Memory — short-term (working) and long-term (persistent) storage, often a vector database.

End-to-end data flow

1. The user submits a prompt on the front-end.
2. The front-end calls the /run-agent endpoint on the back-end.
3. The back-end authenticates, restores the session, and invokes the agent core.
4. The core runs its loop, emitting events (thought, tool_start, tool_end, final_answer).
5. Each event streams back to the back-end.
6. The back-end forwards it to the front-end.
7. The front-end updates the UI — repeating until the task completes.

An example stack

The pattern is framework-agnostic, but one common, capable stack:

• Front-end: Next.js with Tailwind CSS.
• Back-end: FastAPI, for async performance.
• Agent core: LangGraph for the stateful loop, a frontier LLM for reasoning, and Chroma or Pinecone for vector memory.

Keep going

• AI Agents Running Workflows
• Agent-Driven Interfaces with A2UI
• Building an AI Agent with Dual Memory
• Designing Effective Agent Tools

Three rules carry most of the weight: decouple the layers, keep secrets in the back-end, and stream everything — visible reasoning is what makes an agent feel trustworthy.