Build Interactive Agents with Generative UI
by DeepLearning.AI
Get your agent out of the chat box and rendering real UI — charts, forms, canvases — over the open AG-UI protocol.
Overview
Build Interactive Agents with Generative UI is a 90-minute DeepLearning.AI short course taught by Atai Barkai, co-founder and CEO of CopilotKit, the team that created the open AG-UI (Agent-User Interaction) protocol. Its subject is the half of agent engineering most courses skip: the frontend. Eight lessons and five runnable code examples walk up what the course calls the generative-UI spectrum. Lesson 2 wires a Python agent to a React frontend for basic agent chat over AG-UI. Lesson 3 covers controlled generative UI, where you register named React components — a pie chart, a flight card — that the agent renders on demand through tool calls. Lesson 4 introduces declarative generative UI via A2UI, an open spec co-developed with Google, where the agent assembles layouts from a catalog of reusable building blocks rather than from components you hardcoded. Lesson 5 goes open-ended with MCP Apps, letting a connected MCP server supply interfaces the agent composes from scratch. Lesson 6 builds a canvas application in which agent and frontend share live bidirectional state, so the surface is a shared workspace rather than a chat log. The stack is React, Python, a LangChain Deep Agent and a Google ADK agent, joined by CopilotKit and AG-UI, which has first-party integrations across LangGraph, Google ADK, Microsoft Agent Framework, AWS Strands, Mastra, Pydantic AI, Vercel AI SDK, CrewAI, Agno, LlamaIndex and AG2. DeepLearning.AI labels the course Beginner, but it assumes working React and Python.
At a Glance
- Topic
- Agentic
- Level
- Intermediate
- Format
- Course
- Cost
- Freemium
- Duration
- ~1.5 hours (8 lessons, 1h28m of video, 5 code examples), self-paced
- Provider
- DeepLearning.AI
- Hands-on
- Yes — code/exercises
- Certificate
- Available
What You’ll Learn
- ✓Place an interface on the generative-UI spectrum: controlled, declarative, or fully open-ended
- ✓Stream agent state and events into a React frontend using the AG-UI protocol
- ✓Register custom components such as pie charts and flight cards for the agent to render on demand
- ✓Assemble agent-driven layouts declaratively with A2UI's catalog of reusable building blocks
- ✓Expose open-ended, agent-composed interfaces by connecting an agent to MCP Apps
- ✓Build a canvas application where the agent and the frontend share live bidirectional state
- ✓Wire both a LangChain Deep Agent and a Google ADK agent to the same CopilotKit frontend
Highlights
- •Taught by the CopilotKit co-founder whose team authored AG-UI, so it is the protocol's own maintainers explaining the design tradeoffs
- •Covers A2UI, an open declarative-layout spec co-developed with Google, which almost no other course touches yet
- •AG-UI carries first-party integrations across 11 agent frameworks (LangGraph, Google ADK, Microsoft Agent Framework, AWS Strands, Mastra, Pydantic AI, Vercel AI SDK, CrewAI, Agno, LlamaIndex, AG2), so the skills are not CopilotKit-specific
- •One of the very few resources on the frontend half of agent engineering — the directory is saturated with backend orchestration and starved of UI
- •Five runnable code examples inside 90 minutes; you leave with a working fullstack app rather than notes
Who It’s For
Best For
- ✓Fullstack and frontend engineers who have to ship a user-facing surface for an existing agent
- ✓Agent builders whose product is stuck at a chat window and needs charts, forms or approvals
- ✓Teams evaluating AG-UI or A2UI as the transport between their agent backend and their app
Prerequisites
- •Comfort writing React components and managing client-side state
- •Working Python, enough to run and modify a LangChain or Google ADK agent
- •Basic familiarity with LLM tool calling and agent loops — this course does not teach the agent side
FAQ
What is Build Interactive Agents with Generative UI?
A short, hands-on course for engineers whose agents work but whose interface is still a text box. It teaches the three ways an agent can drive a frontend — rendering components you registered, assembling layouts declaratively, or composing interfaces open-endedly — and by the end you have a fullstack React app where a Python agent renders charts, forms and a shared canvas over the AG-UI protocol.
Is Build Interactive Agents with Generative UI free?
Build Interactive Agents with Generative UI offers free content, with paid options for certificates or premium features.
What level is Build Interactive Agents with Generative UI for?
Build Interactive Agents with Generative UI is aimed at a intermediate audience. Recommended background: Comfort writing React components and managing client-side state, Working Python, enough to run and modify a LangChain or Google ADK agent, Basic familiarity with LLM tool calling and agent loops — this course does not teach the agent side.
How long does Build Interactive Agents with Generative UI take?
Expect roughly ~1.5 hours (8 lessons, 1h28m of video, 5 code examples), self-paced. Most learners work through it at their own pace.
What will I learn from Build Interactive Agents with Generative UI?
You'll learn: Place an interface on the generative-UI spectrum: controlled, declarative, or fully open-ended; Stream agent state and events into a React frontend using the AG-UI protocol; Register custom components such as pie charts and flight cards for the agent to render on demand; Assemble agent-driven layouts declaratively with A2UI's catalog of reusable building blocks; Expose open-ended, agent-composed interfaces by connecting an agent to MCP Apps; Build a canvas application where the agent and the frontend share live bidirectional state; Wire both a LangChain Deep Agent and a Google ADK agent to the same CopilotKit frontend.
Topics
Sources
This page was written from 2 sources, 1 on domains other than deeplearning.ai.